@inproceedings{lorince2014supertagger, author = {Lorince, Jared and Zorowitz, Sam and Murdock, Jaimie and Todd, Peter}, title = {“Supertagger” Behavior in Building Folksonomies}, year = {2014}, keywords = {analysis, distribution, folksonomy, supertagger, tag, tagging, toRead} }

%0 = inproceedings %A = Lorince, Jared and Zorowitz, Sam and Murdock, Jaimie and Todd, Peter %D = 2014 %T = “Supertagger” Behavior in Building Folksonomies

We analyze a large-scale snapshot of del.icio.us and investigate how the number of different tags in the system grows as a function of a suitably defined notion of time. We study the temporal evolution of the global vocabulary size, i.e. the number of distinct tags in the entire system, as well as the evolution of local vocabularies, that is the growth of the number of distinct tags used in the context of a given resource or user. In both cases, we find power-law behaviors with exponents smaller than one. Surprisingly, the observed growth behaviors are remarkably regular throughout the entire history of the system and across very different resources being bookmarked. Similar sub-linear laws of growth have been observed in written text, and this qualitative universality calls for an explanation and points in the direction of non-trivial cognitive processes in the complex interaction patterns characterizing collaborative tagging.

@inproceedings{cattuto2007vocabulary, author = {Cattuto, Ciro and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio}, title = {Vocabulary growth in collaborative tagging systems}, year = {2007}, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0704.3316}, keywords = {vocabulary, folksonomy, toread, analysis}, abstract = { We analyze a large-scale snapshot of del.icio.us and investigate how the number of different tags in the system grows as a function of a suitably defined notion of time. We study the temporal evolution of the global vocabulary size, i.e. the number of distinct tags in the entire system, as well as the evolution of local vocabularies, that is the growth of the number of distinct tags used in the context of a given resource or user. In both cases, we find power-law behaviors with exponents smaller than one. Surprisingly, the observed growth behaviors are remarkably regular throughout the entire history of the system and across very different resources being bookmarked. Similar sub-linear laws of growth have been observed in written text, and this qualitative universality calls for an explanation and points in the direction of non-trivial cognitive processes in the complex interaction patterns characterizing collaborative tagging.} }

%0 = inproceedings %A = Cattuto, Ciro and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio %D = 2007 %T = Vocabulary growth in collaborative tagging systems %U = http://www.citebase.org/abstract?id=oai:arXiv.org:0704.3316

This paper aims to combine information about users' self-defined social connections with traditional collaborative filtering (CF) to improve recommendation quality. Specifically, in the following, the users' social connections in consideration were groups. Unlike other studies which utilized groups inferred by data mining technologies, we used the information about the groups in which each user explicitly participated. The group activities are centered on common interests. People join a group to share and acquire information about a topic as a form of community of interest or practice. The information of this group activity may be a good source of information for the members. We tested whether adding the information from the users' own groups or group members to the traditional CF-based recommendations can improve the recommendation quality or not. The information about groups was combined with CF using a mixed hybridization strategy. We evaluated our approach in two ways, using the Citeulike data set and a real user study.

@inproceedings{lee2010using, author = {Lee, Danielle H. and Brusilovsky, Peter}, title = {Using self-defined group activities for improving recommendations in collaborative tagging systems}, booktitle = {Proceedings of the fourth ACM conference on Recommender systems}, publisher = {ACM}, address = {New York, NY, USA}, year = {2010}, pages = {221--224}, url = {http://doi.acm.org/10.1145/1864708.1864752}, doi = {10.1145/1864708.1864752}, isbn = {978-1-60558-906-0}, keywords = {collaborative, folksonomy, item, recommender, social, tagging}, abstract = {This paper aims to combine information about users' self-defined social connections with traditional collaborative filtering (CF) to improve recommendation quality. Specifically, in the following, the users' social connections in consideration were groups. Unlike other studies which utilized groups inferred by data mining technologies, we used the information about the groups in which each user explicitly participated. The group activities are centered on common interests. People join a group to share and acquire information about a topic as a form of community of interest or practice. The information of this group activity may be a good source of information for the members. We tested whether adding the information from the users' own groups or group members to the traditional CF-based recommendations can improve the recommendation quality or not. The information about groups was combined with CF using a mixed hybridization strategy. We evaluated our approach in two ways, using the Citeulike data set and a real user study.} }

%0 = inproceedings %A = Lee, Danielle H. and Brusilovsky, Peter %B = Proceedings of the fourth ACM conference on Recommender systems %C = New York, NY, USA %D = 2010 %I = ACM %T = Using self-defined group activities for improving recommendations in collaborative tagging systems %U = http://doi.acm.org/10.1145/1864708.1864752

@inproceedings{jonsson2007using, author = {Jonsson, Martin}, title = {Using a Folksonomy Approach for Location Tagging in Community Based Presence Systems}, booktitle = {Proceedings of the 2007 International Conference on Mobile Data Management}, publisher = {IEEE Computer Society}, address = {Washington, DC, USA}, year = {2007}, pages = {304--308}, url = {http://portal.acm.org/citation.cfm?id=1548880.1549004}, doi = {10.1109/MDM.2007.64}, isbn = {1-4244-1241-2}, keywords = {folksonomy, location, tagging} }

%0 = inproceedings %A = Jonsson, Martin %B = Proceedings of the 2007 International Conference on Mobile Data Management %C = Washington, DC, USA %D = 2007 %I = IEEE Computer Society %T = Using a Folksonomy Approach for Location Tagging in Community Based Presence Systems %U = http://portal.acm.org/citation.cfm?id=1548880.1549004

Given the rise in popularity of social tagging systems, it seems only natural to ask how efficient is the organically evolved tagging vocabulary in describing underlying document objects? Does this distributed process really provide a way to circumnavigate the traditional "vocabulary problem" with ontology? We analyze a social tagging site, namely del.icio.us, with information theory in order to evaluate the efficiency of this social tagging site for encoding navigation paths to information sources. We show that information theory provides a natural and interesting way to understand this efficiency - or the descriptive, encoding power of tags. Our results indicate the efficiency of tags appears to be waning. We discuss the implications of our findings and provide insight into how our methods can be used to design more usable social tagging software.

@inproceedings{chi2008understanding, author = {Chi, Ed H. and Mytkowicz, Todd}, title = {Understanding the efficiency of social tagging systems using information theory}, booktitle = {Proceedings of the nineteenth ACM conference on Hypertext and hypermedia}, publisher = {ACM}, address = {New York, NY, USA}, year = {2008}, pages = {81--88}, url = {http://doi.acm.org/10.1145/1379092.1379110}, doi = {10.1145/1379092.1379110}, isbn = {978-1-59593-985-2}, keywords = {collaborative, folksonomy, information, social, tagging, theory}, abstract = {Given the rise in popularity of social tagging systems, it seems only natural to ask how efficient is the organically evolved tagging vocabulary in describing underlying document objects? Does this distributed process really provide a way to circumnavigate the traditional "vocabulary problem" with ontology? We analyze a social tagging site, namely del.icio.us, with information theory in order to evaluate the efficiency of this social tagging site for encoding navigation paths to information sources. We show that information theory provides a natural and interesting way to understand this efficiency - or the descriptive, encoding power of tags. Our results indicate the efficiency of tags appears to be waning. We discuss the implications of our findings and provide insight into how our methods can be used to design more usable social tagging software.} }

%0 = inproceedings %A = Chi, Ed H. and Mytkowicz, Todd %B = Proceedings of the nineteenth ACM conference on Hypertext and hypermedia %C = New York, NY, USA %D = 2008 %I = ACM %T = Understanding the efficiency of social tagging systems using information theory %U = http://doi.acm.org/10.1145/1379092.1379110

@inproceedings{jaeschke06trias, author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd}, title = {TRIAS - An Algorithm for Mining Iceberg Tri-Lattices}, booktitle = {Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 06)}, publisher = {IEEE Computer Society}, address = {Hong Kong}, year = {2006}, pages = {907-911}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/jaeschke2006trias.pdf}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.162}, isbn = {0-7695-2701-9}, keywords = {2006, FCA, OntologyHandbook, algorithm, analysis, concept, fca, folksonomies, folksonomy, formal, iceberg, itegpub, lattices, myown, nepomuk, tagging, tri, triadic, trias} }

%0 = inproceedings %A = Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd %B = Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 06) %C = Hong Kong %D = 2006 %I = IEEE Computer Society %T = TRIAS - An Algorithm for Mining Iceberg Tri-Lattices %U = http://www.kde.cs.uni-kassel.de/stumme/papers/2006/jaeschke2006trias.pdf

As the number of resources on the web exceeds by far the number ofdocuments one can track, it becomes increasingly difficult to remainup to date on ones own areas of interest. The problem becomes moresevere with the increasing fraction of multimedia data, from whichit is difficult to extract some conceptual description of theircontents.One way to overcome this problem are social bookmark tools, whichare rapidly emerging on the web. In such systems, users are settingup lightweight conceptual structures called folksonomies, andovercome thus the knowledge acquisition bottleneck. As more and morepeople participate in the effort, the use of a common vocabularybecomes more and more stable. We present an approach for discoveringtopic-specific trends within folksonomies. It is based on adifferential adaptation of the PageRank algorithm to the triadichypergraph structure of a folksonomy. The approach allows for anykind of data, as it does not rely on the internal structure of thedocuments. In particular, this allows to consider different datatypes in the same analysis step. We run experiments on a large-scalereal-world snapshot of a social bookmarking system.

@inproceedings{hotho2006trend, author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, title = {Trend Detection in Folksonomies}, editor = {Avrithis, Yannis S. and Kompatsiaris, Yiannis and Staab, Steffen and O'Connor, Noel E.}, booktitle = {Proc. First International Conference on Semantics And Digital Media Technology (SAMT) }, series = {LNCS}, publisher = {Springer}, address = {Heidelberg}, year = {2006}, volume = {4306}, pages = {56-70}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf}, isbn = {3-540-49335-2}, keywords = {2006, UniK, detection, folkrank, folksonomy, hotho, intranet, itegpub, jaeschke, l3s, myown, nepomuk, pagerank, schmitz, stumme, tagorapub, trend, triadic}, abstract = {As the number of resources on the web exceeds by far the number ofdocuments one can track, it becomes increasingly difficult to remainup to date on ones own areas of interest. The problem becomes moresevere with the increasing fraction of multimedia data, from whichit is difficult to extract some conceptual description of theircontents.One way to overcome this problem are social bookmark tools, whichare rapidly emerging on the web. In such systems, users are settingup lightweight conceptual structures called folksonomies, andovercome thus the knowledge acquisition bottleneck. As more and morepeople participate in the effort, the use of a common vocabularybecomes more and more stable. We present an approach for discoveringtopic-specific trends within folksonomies. It is based on adifferential adaptation of the PageRank algorithm to the triadichypergraph structure of a folksonomy. The approach allows for anykind of data, as it does not rely on the internal structure of thedocuments. In particular, this allows to consider different datatypes in the same analysis step. We run experiments on a large-scalereal-world snapshot of a social bookmarking system.} }

%0 = inproceedings %A = Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd %B = Proc. First International Conference on Semantics And Digital Media Technology (SAMT) %C = Heidelberg %D = 2006 %I = Springer %T = Trend Detection in Folksonomies %U = http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf

As the number of resources on the web exceeds by far the number ofdocuments one can track, it becomes increasingly difficult to remainup to date on ones own areas of interest. The problem becomes moresevere with the increasing fraction of multimedia data, from whichit is difficult to extract some conceptual description of theircontents.One way to overcome this problem are social bookmark tools, whichare rapidly emerging on the web. In such systems, users are settingup lightweight conceptual structures called folksonomies, andovercome thus the knowledge acquisition bottleneck. As more and morepeople participate in the effort, the use of a common vocabularybecomes more and more stable. We present an approach for discoveringtopic-specific trends within folksonomies. It is based on adifferential adaptation of the PageRank algorithm to the triadichypergraph structure of a folksonomy. The approach allows for anykind of data, as it does not rely on the internal structure of thedocuments. In particular, this allows to consider different datatypes in the same analysis step. We run experiments on a large-scalereal-world snapshot of a social bookmarking system.

@inproceedings{hotho2006trend, author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, title = {Trend Detection in Folksonomies}, editor = {Avrithis, Yannis S. and Kompatsiaris, Yiannis and Staab, Steffen and O'Connor, Noel E.}, booktitle = {Proc. First International Conference on Semantics And Digital Media Technology (SAMT) }, series = {LNCS}, publisher = {Springer}, address = {Heidelberg}, year = {2006}, volume = {4306}, pages = {56-70}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf}, isbn = {3-540-49335-2}, keywords = {intranet, 2006, trend, pagerank, hotho, schmitz, jaeschke, l3s, itegpub, detection, triadic, stumme, nepomuk, folksonomy, tagorapub, folkrank, UniK}, abstract = {As the number of resources on the web exceeds by far the number ofdocuments one can track, it becomes increasingly difficult to remainup to date on ones own areas of interest. The problem becomes moresevere with the increasing fraction of multimedia data, from whichit is difficult to extract some conceptual description of theircontents.One way to overcome this problem are social bookmark tools, whichare rapidly emerging on the web. In such systems, users are settingup lightweight conceptual structures called folksonomies, andovercome thus the knowledge acquisition bottleneck. As more and morepeople participate in the effort, the use of a common vocabularybecomes more and more stable. We present an approach for discoveringtopic-specific trends within folksonomies. It is based on adifferential adaptation of the PageRank algorithm to the triadichypergraph structure of a folksonomy. The approach allows for anykind of data, as it does not rely on the internal structure of thedocuments. In particular, this allows to consider different datatypes in the same analysis step. We run experiments on a large-scalereal-world snapshot of a social bookmarking system.} }

%0 = inproceedings %A = Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd %B = Proc. First International Conference on Semantics And Digital Media Technology (SAMT) %C = Heidelberg %D = 2006 %I = Springer %T = Trend Detection in Folksonomies %U = http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf

@inproceedings{Kim2008, author = {Kim, Hak Lae and Scerri, Simon and Breslin, John G. and Decker, Stefan and Kim, Hong Gee}, title = {The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies}, booktitle = {Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications}, publisher = {Dublin Core Metadata Initiative}, address = {Berlin, Deutschland}, year = {2008}, pages = {128--137}, keywords = {folksonomy, ontology, semantic, tag, tagging, taggingsurvey, toread} }

%0 = inproceedings %A = Kim, Hak Lae and Scerri, Simon and Breslin, John G. and Decker, Stefan and Kim, Hong Gee %B = Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications %C = Berlin, Deutschland %D = 2008 %I = Dublin Core Metadata Initiative %T = The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies

Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.

@article{benz2010social, author = {Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Mitzlaff, Folke and Schmitz, Christoph and Stumme, Gerd}, title = {The Social Bookmark and Publication Management System BibSonomy}, journal = {The VLDB Journal}, publisher = {Springer}, address = {Berlin / Heidelberg}, year = {2010}, volume = {19}, number = {6}, pages = {849--875}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/benz2010social.pdf}, doi = {10.1007/s00778-010-0208-4}, issn = {1066-8888}, keywords = {2010, collaborative, folksonomy, management, publication, social, system, vldb}, abstract = {Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.} }

%0 = article %A = Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Mitzlaff, Folke and Schmitz, Christoph and Stumme, Gerd %C = Berlin / Heidelberg %D = 2010 %I = Springer %T = The Social Bookmark and Publication Management System BibSonomy %U = http://www.kde.cs.uni-kassel.de/pub/pdf/benz2010social.pdf

Collaborative tagging systems are popular tools for organization, sharing and retrieval of web resources. Their success is due to their freedom and simplicity of use. To post a resource, the user should only define a set of tags that would position the resource in the system's data structure - folksonomy. This data structure can serve as a rich source of information about relations between tags and concepts they represent. To make use of information collaboratively added to folksonomies, we need to understand how users make tagging decisions. Three factors that are believed to influence user tagging decisions are: the tags used by other users, the organization of user's personal repository and the knowledge model shared between users. In our work we examine the role of another potential factor - resource title. Despite all the advantages of tags, tagging is a tedious process. To minimize the effort, users are likely to tag with keywords that are easily available. We show that resource title, as a source of useful tags, is easy to access and comprehend. Given a choice of two tags with the same meaning, users are likely to be influenced by their presence in the title. However, a factor that seems to have stronger impact on users' tagging decisions is maintaining the consistency of the personal profile of tags. The results of our study reveal a new, less idealistic picture of collaborative tagging systems, in which the collaborative aspect seems to be less important than personal gains and convenience.

@inproceedings{lipczak2010impact, author = {Lipczak, Marek and Milios, Evangelos}, title = {The Impact of Resource Title on Tags in Collaborative Tagging Systems}, booktitle = {Proceedings of the 21st ACM Conference on Hypertext and Hypermedia}, series = {HT '10}, publisher = {ACM}, address = {New York, NY, USA}, year = {2010}, pages = {179--188}, url = {http://doi.acm.org/10.1145/1810617.1810648}, doi = {10.1145/1810617.1810648}, isbn = {978-1-4503-0041-4}, keywords = {baarbeit, folksonomy, impact_title, social, tagging, toread}, abstract = {Collaborative tagging systems are popular tools for organization, sharing and retrieval of web resources. Their success is due to their freedom and simplicity of use. To post a resource, the user should only define a set of tags that would position the resource in the system's data structure -- folksonomy. This data structure can serve as a rich source of information about relations between tags and concepts they represent. To make use of information collaboratively added to folksonomies, we need to understand how users make tagging decisions. Three factors that are believed to influence user tagging decisions are: the tags used by other users, the organization of user's personal repository and the knowledge model shared between users. In our work we examine the role of another potential factor -- resource title. Despite all the advantages of tags, tagging is a tedious process. To minimize the effort, users are likely to tag with keywords that are easily available. We show that resource title, as a source of useful tags, is easy to access and comprehend. Given a choice of two tags with the same meaning, users are likely to be influenced by their presence in the title. However, a factor that seems to have stronger impact on users' tagging decisions is maintaining the consistency of the personal profile of tags. The results of our study reveal a new, less idealistic picture of collaborative tagging systems, in which the collaborative aspect seems to be less important than personal gains and convenience.} }

%0 = inproceedings %A = Lipczak, Marek and Milios, Evangelos %B = Proceedings of the 21st ACM Conference on Hypertext and Hypermedia %C = New York, NY, USA %D = 2010 %I = ACM %T = The Impact of Resource Title on Tags in Collaborative Tagging Systems %U = http://doi.acm.org/10.1145/1810617.1810648

@inproceedings{krause2008antisocialb, author = {Krause, Beate and Schmitz, Christoph and Hotho, Andreas and Stumme, Gerd}, title = {The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems}, booktitle = {Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web}, year = {2008}, url = {http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf}, keywords = {2.0, 2008, bookmarking, folksonomies, folksonomy, itegpub, myown, social, spam, systems, tagger, tagorapub, web, web2.0} }

%0 = inproceedings %A = Krause, Beate and Schmitz, Christoph and Hotho, Andreas and Stumme, Gerd %B = Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web %D = 2008 %T = The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems %U = http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf

@inproceedings{krause2008antisocialb, author = {Krause, Beate and Schmitz, Christoph and Hotho, Andreas and Stumme, Gerd}, title = {The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems}, booktitle = {Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web}, year = {2008}, url = {http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf}, keywords = {2008, systems, bookmarking, web, tagger, 2.0, itegpub, social, web2.0, folksonomy, folksonomies, tagorapub, spam} }

%0 = inproceedings %A = Krause, Beate and Schmitz, Christoph and Hotho, Andreas and Stumme, Gerd %B = Proc. of the Fourth International Workshop on Adversarial Information Retrieval on the Web %D = 2008 %T = The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems %U = http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf

The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a �rst evaluation of two exemplarily deployed recommendation methods.

@inproceedings{jaeschke2009testing, author = {Jäschke, Robert and Eisterlehner, Folke and Hotho, Andreas and Stumme, Gerd}, title = {Testing and Evaluating Tag Recommenders in a Live System}, booktitle = {RecSys '09: Proceedings of the 2009 ACM Conference on Recommender Systems}, publisher = {ACM}, address = {New York, NY, USA}, year = {2009}, note = {(to appear)}, keywords = {baarbeit, evaluating, folksonomy, framework, recommender, test, testing, toread}, abstract = {The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a �rst evaluation of two exemplarily deployed recommendation methods.} }

%0 = inproceedings %A = Jäschke, Robert and Eisterlehner, Folke and Hotho, Andreas and Stumme, Gerd %B = RecSys '09: Proceedings of the 2009 ACM Conference on Recommender Systems %C = New York, NY, USA %D = 2009 %I = ACM %T = Testing and Evaluating Tag Recommenders in a Live System

Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs, videos and other content. In ubiquitous computing environment, users access data through various kinds of mobile terminals. Therefore users want more accurate materials because of expensive communication cost or the useless results due to abuse of tags. In this paper, we first describe current limitation of tagging services. We then describe the system (TagPlus) we implemented to minimize ambiguity due to no synonym control. Finally, we give experimental results.

@inproceedings{lee2007tagplus, author = {Lee, Sun-Sook and Yong, Hwan-Seung}, title = {TagPlus: A Retrieval System using Synonym Tag in Folksonomy}, booktitle = {Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering}, series = {MUE '07}, publisher = {IEEE Computer Society}, address = {Washington, DC, USA}, year = {2007}, pages = {294--298}, url = {http://dx.doi.org/10.1109/MUE.2007.201}, doi = {http://dx.doi.org/10.1109/MUE.2007.201}, isbn = {0-7695-2777-9}, keywords = {folksonomy, information_retrieval, ol_web2.0, synonyms, methods_synonyms}, abstract = {Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs, videos and other content. In ubiquitous computing environment, users access data through various kinds of mobile terminals. Therefore users want more accurate materials because of expensive communication cost or the useless results due to abuse of tags. In this paper, we first describe current limitation of tagging services. We then describe the system (TagPlus) we implemented to minimize ambiguity due to no synonym control. Finally, we give experimental results.} }

%0 = inproceedings %A = Lee, Sun-Sook and Yong, Hwan-Seung %B = Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering %C = Washington, DC, USA %D = 2007 %I = IEEE Computer Society %T = TagPlus: A Retrieval System using Synonym Tag in Folksonomy %U = http://dx.doi.org/10.1109/MUE.2007.201

Learning-to-rank methods automatically generate ranking functions which can be used for ordering unknown resources according to their relevance for a specific search query. The training data to construct such a model consists of features describing a document-query-pair as well as relevance scores indicating how important the document is for the query. In general, these relevance scores are derived by asking experts to manually assess search results or by exploiting user search behaviour such as click data. The human evaluation of ranking results gives explicit relevance scores, but it is expensive to obtain. Clickdata can be logged from the user interaction with a search engine, but the feedback is noisy. In this paper, we want to explore a novel source of implicit feedback for web search: tagging data. Creating relevance feedback from tagging data leads to a further source of implicit relevance feedback which helps improve the reliability of automatically generated relevance scores and therefore the quality of learning-to-rank models.

@inproceedings{navarrobullock2011tagging, author = {Navarro Bullock, Beate and Jäschke, Robert and Hotho, Andreas}, title = {Tagging data as implicit feedback for learning-to-rank}, booktitle = {Proceedings of the ACM WebSci Conference}, address = {New York, NY, USA}, year = {2011}, pages = {1--4}, url = {http://journal.webscience.org/463/}, keywords = {2011, bookmarking, folksonomy, letor, myown, ranking, social, tagging}, abstract = {Learning-to-rank methods automatically generate ranking functions which can be used for ordering unknown resources according to their relevance for a specific search query. The training data to construct such a model consists of features describing a document-query-pair as well as relevance scores indicating how important the document is for the query. In general, these relevance scores are derived by asking experts to manually assess search results or by exploiting user search behaviour such as click data. The human evaluation of ranking results gives explicit relevance scores, but it is expensive to obtain. Clickdata can be logged from the user interaction with a search engine, but the feedback is noisy. In this paper, we want to explore a novel source of implicit feedback for web search: tagging data. Creating relevance feedback from tagging data leads to a further source of implicit relevance feedback which helps improve the reliability of automatically generated relevance scores and therefore the quality of learning-to-rank models.} }

%0 = inproceedings %A = Navarro Bullock, Beate and Jäschke, Robert and Hotho, Andreas %B = Proceedings of the ACM WebSci Conference %C = New York, NY, USA %D = 2011 %T = Tagging data as implicit feedback for learning-to-rank %U = http://journal.webscience.org/463/

@inproceedings{jaeschke07tagKdml, author = {Jaeschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, title = {Tag Recommendations in Folksonomies}, editor = {Hinneburg, Alexander}, booktitle = {Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)}, publisher = {Martin-Luther-Universität Halle-Wittenberg}, year = {2007}, pages = {13-20}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2007/jaeschke07tagrecommendationsKDML.pdf}, isbn = {978-3-86010-907-6}, keywords = {2007, bookmarking, collaborative, filtering, folksonomy, itegpub, l3s, myown, recommender, social} }

%0 = inproceedings %A = Jaeschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd %B = Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007) %D = 2007 %I = Martin-Luther-Universität Halle-Wittenberg %T = Tag Recommendations in Folksonomies %U = http://www.kde.cs.uni-kassel.de/stumme/papers/2007/jaeschke07tagrecommendationsKDML.pdf

With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.

@inproceedings{mueller2013recommendations, author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd}, title = {Tag Recommendations for SensorFolkSonomies}, booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings}, publisher = {CEUR-WS}, address = {Aachen, Germany}, year = {2013}, volume = {1066}, url = {http://ceur-ws.org/Vol-1066/}, keywords = {2013, everyaware, folksonomy, myown, recommender, sensor, tag}, abstract = {With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.} }

%0 = inproceedings %A = Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd %B = Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings %C = Aachen, Germany %D = 2013 %I = CEUR-WS %T = Tag Recommendations for SensorFolkSonomies %U = http://ceur-ws.org/Vol-1066/

With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.

@inproceedings{mueller2013recommendations, author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd}, title = {Tag Recommendations for SensorFolkSonomies}, booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings}, publisher = {ACM}, year = {2013}, pages = {New York, NY, USA}, note = {accepted for publication}, keywords = {2013, RecSys, everyaware, folksonomy, iteg, itegpub, l3s, myown, recommendation, rsweb, sensor, sitc, tag, widenoise}, abstract = {With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.} }

%0 = inproceedings %A = Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd %B = Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings %D = 2013 %I = ACM %T = Tag Recommendations for SensorFolkSonomies

With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.

@inproceedings{mueller2013recommendations, author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd}, title = {Tag Recommendations for SensorFolkSonomies}, booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings}, publisher = {ACM}, year = {2013}, pages = {New York, NY, USA}, note = {accepted for publication}, keywords = {2013, RecSys, everyaware, folksonomy, myown, recommendation, rsweb, sensor, tag, widenoise}, abstract = {With the rising popularity of smart mobile devices, sensor data-based
pplications have become more and more popular. Their users record
ata during their daily routine or specifically for certain events.
he application WideNoise Plus allows users to record sound samples
nd to annotate them with perceptions and tags. The app is being
sed to document and map the soundscape all over the world. The procedure
f recording, including the assignment of tags, has to be as easy-to-use
s possible. We therefore discuss the application of tag recommender
lgorithms in this particular scenario. We show, that this task is
undamentally different from the well-known tag recommendation problem
n folksonomies as users do no longer tag fix resources but rather
ensory data and impressions. The scenario requires efficient recommender
lgorithms that are able to run on the mobile device, since Internet
onnectivity cannot be assumed to be available. Therefore, we evaluate
he performance of several tag recommendation algorithms and discuss
heir applicability in the mobile sensing use-case.} }

%0 = inproceedings %A = Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd %B = Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings %D = 2013 %I = ACM %T = Tag Recommendations for SensorFolkSonomies

This paper presents SWE-FE: a suite of methods to extend folksonomies to the worldwide Sensor Web in order to tackle the emergent data rich information poor (DRIP) syndrome afflicting most geospatial applications on the Internet. SWE-FE leverages the geospatial information associated with three key components of such collaborative tagging systems: tags, resources and users. Specifically, SWE-FE provides algorithms for: i) suggesting tags for users during the tag input stage; ii) generating tag maps which provides for serendipitous browsing; and iii) personalized searching within the folksonomy. We implement SWE-FE on the GeoCENS Sensor Web platform as a case study for assessing the efficacy of our methods. We outline the evaluation framework that we are currently employing to carry out this assessment.

@inproceedings{rezel2010swefe, author = {Rezel, R. and Liang, S.}, title = {SWE-FE: Extending folksonomies to the Sensor Web}, booktitle = {2010 International Symposium on Collaborative Technologies and Systems (CTS)}, publisher = {IEEE}, year = {2010}, pages = {349--356}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5478494}, doi = {10.1109/CTS.2010.5478494}, keywords = {collaborative, everyaware, folksonomy, sensor, tagging, taggingsurvey, toread}, abstract = {This paper presents SWE-FE: a suite of methods to extend folksonomies to the worldwide Sensor Web in order to tackle the emergent data rich information poor (DRIP) syndrome afflicting most geospatial applications on the Internet. SWE-FE leverages the geospatial information associated with three key components of such collaborative tagging systems: tags, resources and users. Specifically, SWE-FE provides algorithms for: i) suggesting tags for users during the tag input stage; ii) generating tag maps which provides for serendipitous browsing; and iii) personalized searching within the folksonomy. We implement SWE-FE on the GeoCENS Sensor Web platform as a case study for assessing the efficacy of our methods. We outline the evaluation framework that we are currently employing to carry out this assessment.} }

%0 = inproceedings %A = Rezel, R. and Liang, S. %B = 2010 International Symposium on Collaborative Technologies and Systems (CTS) %D = 2010 %I = IEEE %T = SWE-FE: Extending folksonomies to the Sensor Web %U = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5478494

Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.

@inproceedings{cattuto2008semantic, author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Semantic Grounding of Tag Relatedness in Social Bookmarking Systems}, booktitle = {The Semantic Web - ISWC 2008}, series = {Lecture Notes in Computer Science}, publisher = {Springer Berlin / Heidelberg}, year = {2008}, volume = {5318}, pages = {615--631}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/cattuto2008semantica.pdf}, doi = {10.1007/978-3-540-88564-1_39}, isbn = {978-3-540-88563-4}, keywords = {2008, folksonomy, grounding, iswc2008, myown, semantic, sw, tag, tagging, taggingsurvey, webzu}, abstract = {Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.} }

%0 = inproceedings %A = Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd %B = The Semantic Web - ISWC 2008 %D = 2008 %I = Springer Berlin / Heidelberg %T = Semantic Grounding of Tag Relatedness in Social Bookmarking Systems %U = http://www.kde.cs.uni-kassel.de/pub/pdf/cattuto2008semantica.pdf

We present an approach that efficiently identifies the semantic meanings and contexts of social tags within a particular folksonomy, and exploits them to build contextualised tag-based user and item profiles. We apply our approach to a dataset obtained from Delicious social bookmarking system, and evaluate it through two experiments: a user study consisting of manual judgements of tag disambiguation and contextualisation cases, and an offline study measuring the performance of several tag-powered item recommendation algorithms by using contextualised profiles. The results obtained show that our approach is able to accurately determine the actual semantic meanings and contexts of tag annotations, and allow item recommenders to achieve better precision and recall on their predictions.

@incollection{cantador2011semantic, author = {Cantador, Iván and Bellogín, Alejandro and Fernández-Tobías, Ignacio and López-Hernández, Sergio}, title = {Semantic Contextualisation of Social Tag-Based Profiles and Item Recommendations}, editor = {Huemer, Christian and Setzer, Thomas and Aalst, Wil and Mylopoulos, John and Rosemann, Michael and Shaw, Michael J. and Szyperski, Clemens}, booktitle = {E-Commerce and Web Technologies}, series = {Lecture Notes in Business Information Processing}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2011}, volume = {85}, pages = {101--113}, url = {http://dx.doi.org/10.1007/978-3-642-23014-1_9}, doi = {10.1007/978-3-642-23014-1_9}, isbn = {978-3-642-23014-1}, keywords = {folksonomy, item, recommender, semantics, tagging}, abstract = {We present an approach that efficiently identifies the semantic meanings and contexts of social tags within a particular folksonomy, and exploits them to build contextualised tag-based user and item profiles. We apply our approach to a dataset obtained from Delicious social bookmarking system, and evaluate it through two experiments: a user study consisting of manual judgements of tag disambiguation and contextualisation cases, and an offline study measuring the performance of several tag-powered item recommendation algorithms by using contextualised profiles. The results obtained show that our approach is able to accurately determine the actual semantic meanings and contexts of tag annotations, and allow item recommenders to achieve better precision and recall on their predictions.} }

%0 = incollection %A = Cantador, Iván and Bellogín, Alejandro and Fernández-Tobías, Ignacio and López-Hernández, Sergio %B = E-Commerce and Web Technologies %C = Berlin/Heidelberg %D = 2011 %I = Springer %T = Semantic Contextualisation of Social Tag-Based Profiles and Item Recommendations %U = http://dx.doi.org/10.1007/978-3-642-23014-1_9

@inproceedings{abbasi2009richvsm, author = {Abbasi, Rabeeh and Staab, Steffen}, title = {RichVSM: enRiched Vector Space Models for Folksonomies}, booktitle = {HyperText'09: Proceedings of 20th ACM conference on Hypertext and Hypermedia}, year = {2009}, keywords = {folksonomy, richvsm, vector_space} }

%0 = inproceedings %A = Abbasi, Rabeeh and Staab, Steffen %B = HyperText'09: Proceedings of 20th ACM conference on Hypertext and Hypermedia %D = 2009 %T = RichVSM: enRiched Vector Space Models for Folksonomies

Collaborative tagging applications enable users to annotate online resources with user-generated keywords. The collection of these annotations and the way they connect users and resources produce a rich information space for users to explore. However the size, complexity and chaotic structure of these systems hamper users as they search for information. Recommenders can assist the user by suggesting resources, tags or even other users. Previous work has demonstrated that an integrative approach which exploits all three dimensions of the data (users, resources, tags) produce superior results in tag recommendation. We extend this integrative philosophy to resource recommendation. Specifically, we propose an approach for designing weighted linear hybrid resource recommenders. Through extensive experimentation on two large real world datasets, we show that the hybrid recommenders surpass the effectiveness of their constituent components while inheriting their simplicity, computational efficiency and explanatory capacity. We further introduce the notion of information channels which describe the interaction of the three dimensions. Information channels can be used to explain the effectiveness of individual recommenders or explain the relative contribution of components in the hybrid recommender.

@incollection{gemmell2010resource, author = {Gemmell, Jonathan and Schimoler, Thomas and Mobasher, Bamshad and Burke, Robin}, title = {Resource Recommendation in Collaborative Tagging Applications}, editor = {Buccafurri, Francesco and Semeraro, Giovanni}, booktitle = {E-Commerce and Web Technologies}, series = {Lecture Notes in Business Information Processing}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2010}, volume = {61}, pages = {1--12}, url = {http://dx.doi.org/10.1007/978-3-642-15208-5_1}, doi = {10.1007/978-3-642-15208-5_1}, isbn = {978-3-642-15208-5}, keywords = {collaborative, folksonomy, item, recommender, tagging}, abstract = {Collaborative tagging applications enable users to annotate online resources with user-generated keywords. The collection of these annotations and the way they connect users and resources produce a rich information space for users to explore. However the size, complexity and chaotic structure of these systems hamper users as they search for information. Recommenders can assist the user by suggesting resources, tags or even other users. Previous work has demonstrated that an integrative approach which exploits all three dimensions of the data (users, resources, tags) produce superior results in tag recommendation. We extend this integrative philosophy to resource recommendation. Specifically, we propose an approach for designing weighted linear hybrid resource recommenders. Through extensive experimentation on two large real world datasets, we show that the hybrid recommenders surpass the effectiveness of their constituent components while inheriting their simplicity, computational efficiency and explanatory capacity. We further introduce the notion of information channels which describe the interaction of the three dimensions. Information channels can be used to explain the effectiveness of individual recommenders or explain the relative contribution of components in the hybrid recommender.} }

%0 = incollection %A = Gemmell, Jonathan and Schimoler, Thomas and Mobasher, Bamshad and Burke, Robin %B = E-Commerce and Web Technologies %C = Berlin/Heidelberg %D = 2010 %I = Springer %T = Resource Recommendation in Collaborative Tagging Applications %U = http://dx.doi.org/10.1007/978-3-642-15208-5_1

Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.

@book{balbymarinho2012recommender, author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.}, title = {Recommender Systems for Social Tagging Systems}, series = {SpringerBriefs in Electrical and Computer Engineering}, publisher = {Springer}, year = {2012}, url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8}, doi = {10.1007/978-1-4614-1894-8}, isbn = {978-1-4614-1893-1}, keywords = {2012, bookmarking, collaborative, folksonomy, info20, itegpub, l3s, myown, recommender, social, tagging, tagging,2012}, abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.} }

%0 = book %A = Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P. %D = 2012 %I = Springer %T = Recommender Systems for Social Tagging Systems %U = http://link.springer.com/book/10.1007/978-1-4614-1894-8

Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.

@book{balbymarinho2012recommender, author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.}, title = {Recommender Systems for Social Tagging Systems}, series = {SpringerBriefs in Electrical and Computer Engineering}, publisher = {Springer}, year = {2012}, url = {http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1}, isbn = {978-1-4614-1893-1}, keywords = {2012, bookmarking, collaborative, folksonomy, myown, recommender, social, tagging}, abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.} }

%0 = book %A = Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P. %D = 2012 %I = Springer %T = Recommender Systems for Social Tagging Systems %U = http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1

Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.

@book{balbymarinho2012recommender, author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.}, title = {Recommender Systems for Social Tagging Systems}, series = {SpringerBriefs in Electrical and Computer Engineering}, publisher = {Springer}, year = {2012}, url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8}, doi = {10.1007/978-1-4614-1894-8}, isbn = {978-1-4614-1893-1}, keywords = {2012, bookmarking, collaborative, folksonomy, myown, recommender, social, tagging}, abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.} }

%0 = book %A = Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P. %D = 2012 %I = Springer %T = Recommender Systems for Social Tagging Systems %U = http://link.springer.com/book/10.1007/978-1-4614-1894-8

Recommender systems belong to a class of personalized information filtering technologies that aim to identify which items in a collection might be of interest to a particular user. Recommendations can be made using a variety of information sources related to both the user and the items: past user preferences, demographic information, item popularity, the metadata characteristics of the products, etc. Social bookmarking websites, with their emphasis on open collaborative information access, offer an ideal scenario for the application of recommender systems technology. They allow users to manage their favorite bookmarks online through a web interface and, in many cases, allow their users to tag the content they have added to the system with keywords. The underlying application then makes all information sharable among users. Examples of social bookmarking services include Delicious, Diigo, Furl, CiteULike, and BibSonomy. In my Ph.D. thesis I describe the work I have done on item recommendation for social bookmarking, i.e., recommending interesting bookmarks to users based on the content they bookmarked in the past. In my experiments I distinguish between two types of information sources. The first one is usage data contained in the folksonomy, which represents the past selections and transactions of all users, i.e., who added which items, and with what tags. The second information source is the metadata describing the bookmarks or articles on a social bookmarking website, such as title, description, authorship, tags, and temporal and publication-related metadata. I compare and combine the content-based aspect with the more common usage-based approaches. I evaluate my approaches on four data sets constructed from three different social bookmarking websites: BibSonomy, CiteULike, and Delicious. In addition, I investigate different combination methods for combining different algorithms and show which of those methods can successfully improve recommendation performance. Finally, I consider two growing pains that accompany the maturation of social bookmarking websites: spam and duplicate content. I examine how widespread each of these problems are for social bookmarking and how to develop effective automatic methods for detecting such unwanted content. Finally, I investigate the influence spam and duplicate content can have on item recommendation.

@phdthesis{bogers2009recommender, author = {Bogers, Toine}, title = {Recommender Systems for Social Bookmarking}, school = {Tilburg University}, address = {Tilburg, The Netherlands}, year = {2009}, url = {http://ilk.uvt.nl/~toine/phd-thesis/}, keywords = {bookmarking, dissertation, folksonomy, recommender, social, tagging, taggingsurvey}, abstract = {Recommender systems belong to a class of personalized information filtering technologies that aim to identify which items in a collection might be of interest to a particular user. Recommendations can be made using a variety of information sources related to both the user and the items: past user preferences, demographic information, item popularity, the metadata characteristics of the products, etc. Social bookmarking websites, with their emphasis on open collaborative information access, offer an ideal scenario for the application of recommender systems technology. They allow users to manage their favorite bookmarks online through a web interface and, in many cases, allow their users to tag the content they have added to the system with keywords. The underlying application then makes all information sharable among users. Examples of social bookmarking services include Delicious, Diigo, Furl, CiteULike, and BibSonomy. In my Ph.D. thesis I describe the work I have done on item recommendation for social bookmarking, i.e., recommending interesting bookmarks to users based on the content they bookmarked in the past. In my experiments I distinguish between two types of information sources. The first one is usage data contained in the folksonomy, which represents the past selections and transactions of all users, i.e., who added which items, and with what tags. The second information source is the metadata describing the bookmarks or articles on a social bookmarking website, such as title, description, authorship, tags, and temporal and publication-related metadata. I compare and combine the content-based aspect with the more common usage-based approaches. I evaluate my approaches on four data sets constructed from three different social bookmarking websites: BibSonomy, CiteULike, and Delicious. In addition, I investigate different combination methods for combining different algorithms and show which of those methods can successfully improve recommendation performance. Finally, I consider two growing pains that accompany the maturation of social bookmarking websites: spam and duplicate content. I examine how widespread each of these problems are for social bookmarking and how to develop effective automatic methods for detecting such unwanted content. Finally, I investigate the influence spam and duplicate content can have on item recommendation. } }

%0 = phdthesis %A = Bogers, Toine %C = Tilburg, The Netherlands %D = 2009 %T = Recommender Systems for Social Bookmarking %U = http://ilk.uvt.nl/~toine/phd-thesis/

Recommender systems belong to a class of personalized information filtering technologies that aim to identify which items in a collection might be of interest to a particular user. Recommendations can be made using a variety of information sources related to both the user and the items: past user preferences, demographic information, item popularity, the metadata characteristics of the products, etc. Social bookmarking websites, with their emphasis on open collaborative information access, offer an ideal scenario for the application of recommender systems technology. They allow users to manage their favorite bookmarks online through a web interface and, in many cases, allow their users to tag the content they have added to the system with keywords. The underlying application then makes all information sharable among users. Examples of social bookmarking services include Delicious, Diigo, Furl, CiteULike, and BibSonomy.In my Ph.D. thesis I describe the work I have done on item recommendation for social bookmarking, i.e., recommending interesting bookmarks to users based on the content they bookmarked in the past. In my experiments I distinguish between two types of information sources. The first one is usage data contained in the folksonomy, which represents the past selections and transactions of all users, i.e., who added which items, and with what tags. The second information source is the metadata describing the bookmarks or articles on a social bookmarking website, such as title, description, authorship, tags, and temporal and publication-related metadata. I compare and combine the content-based aspect with the more common usage-based approaches. I evaluate my approaches on four data sets constructed from three different social bookmarking websites: BibSonomy, CiteULike, and Delicious. In addition, I investigate different combination methods for combining different algorithms and show which of those methods can successfully improve recommendation performance.Finally, I consider two growing pains that accompany the maturation of social bookmarking websites: spam and duplicate content. I examine how widespread each of these problems are for social bookmarking and how to develop effective automatic methods for detecting such unwanted content. Finally, I investigate the influence spam and duplicate content can have on item recommendation.

@phdthesis{bogers2009recommender, author = {Bogers, Toine}, title = {Recommender Systems for Social Bookmarking}, school = {Tilburg University}, address = {Tilburg, The Netherlands}, year = {2009}, url = {http://ilk.uvt.nl/~toine/phd-thesis/}, keywords = {bookmarking, tagging, social, item, recommender, folksonomy, from:jaeschke, thesis}, abstract = {Recommender systems belong to a class of personalized information filtering technologies that aim to identify which items in a collection might be of interest to a particular user. Recommendations can be made using a variety of information sources related to both the user and the items: past user preferences, demographic information, item popularity, the metadata characteristics of the products, etc. Social bookmarking websites, with their emphasis on open collaborative information access, offer an ideal scenario for the application of recommender systems technology. They allow users to manage their favorite bookmarks online through a web interface and, in many cases, allow their users to tag the content they have added to the system with keywords. The underlying application then makes all information sharable among users. Examples of social bookmarking services include Delicious, Diigo, Furl, CiteULike, and BibSonomy.In my Ph.D. thesis I describe the work I have done on item recommendation for social bookmarking, i.e., recommending interesting bookmarks to users based on the content they bookmarked in the past. In my experiments I distinguish between two types of information sources. The first one is usage data contained in the folksonomy, which represents the past selections and transactions of all users, i.e., who added which items, and with what tags. The second information source is the metadata describing the bookmarks or articles on a social bookmarking website, such as title, description, authorship, tags, and temporal and publication-related metadata. I compare and combine the content-based aspect with the more common usage-based approaches. I evaluate my approaches on four data sets constructed from three different social bookmarking websites: BibSonomy, CiteULike, and Delicious. In addition, I investigate different combination methods for combining different algorithms and show which of those methods can successfully improve recommendation performance.Finally, I consider two growing pains that accompany the maturation of social bookmarking websites: spam and duplicate content. I examine how widespread each of these problems are for social bookmarking and how to develop effective automatic methods for detecting such unwanted content. Finally, I investigate the influence spam and duplicate content can have on item recommendation. } }

%0 = phdthesis %A = Bogers, Toine %C = Tilburg, The Netherlands %D = 2009 %T = Recommender Systems for Social Bookmarking %U = http://ilk.uvt.nl/~toine/phd-thesis/

Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well.

@article{benz2010query, author = {Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Stumme, Gerd}, title = {Query Logs as Folksonomies}, journal = {Datenbank-Spektrum}, year = {2010}, volume = {10}, number = {1}, pages = {15--24}, url = {http://dx.doi.org/10.1007/s13222-010-0004-8}, keywords = {2010, folksonomy, itegpub, logsonomy, myown}, abstract = {Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well. } }

%0 = article %A = Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Stumme, Gerd %D = 2010 %T = Query Logs as Folksonomies %U = http://dx.doi.org/10.1007/s13222-010-0004-8

Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well.

@article{benz2010query, author = {Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Stumme, Gerd}, title = {Query Logs as Folksonomies}, journal = {Datenbank-Spektrum}, year = {2010}, volume = {10}, number = {1}, pages = {15--24}, url = {http://dx.doi.org/10.1007/s13222-010-0004-8}, keywords = {2010, folksonomy, itegpub, logsonomy, myown}, abstract = {Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well. } }

%0 = article %A = Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Stumme, Gerd %D = 2010 %T = Query Logs as Folksonomies %U = http://dx.doi.org/10.1007/s13222-010-0004-8

@inproceedings{helic2011pragmatic, author = {Helic, D. and Strohmaier, M. and Trattner, C. and Muhr, M. and Lerman, K.}, title = {Pragmatic Evaluation of Folksonomies}, booktitle = {20th International World Wide Web Conference (WWW2011), Hyderabad, India, March 28 - April 1, ACM}, year = {2011}, keywords = {evaluation, folksonomy, pragmatic} }

%0 = inproceedings %A = Helic, D. and Strohmaier, M. and Trattner, C. and Muhr, M. and Lerman, K. %B = 20th International World Wide Web Conference (WWW2011), Hyderabad, India, March 28 - April 1, ACM %D = 2011 %T = Pragmatic Evaluation of Folksonomies

This paper looks inside FolkRank, one of the well-known folksonomy-based algorithms, to present its fundamental properties and promising possibilities for improving performance in tag recommendations. Moreover, we introduce a new way to compute a differential approach in FolkRank by representing it as a linear combination of the personalized PageRank vectors. By the linear combination, we present FolkRank's probabilistic interpretation that grasps how FolkRank works on a folksonomy graph in terms of the random surfer model. We also propose new FolkRank-like methods for tag recommendations to efficiently compute tags' rankings and thus reduce expensive computational cost of FolkRank. We show that the FolkRank approaches are feasible to recommend tags in real-time scenarios as well. The experimental evaluations show that the proposed methods provide fast tag recommendations with reasonable quality, as compared to FolkRank. Additionally, we discuss the diversity of the top n tags recommended by FolkRank and its variants.

@inproceedings{kim2011personalized, author = {Kim, Heung-Nam and El Saddik, Abdulmotaleb}, title = {Personalized PageRank vectors for tag recommendations: inside FolkRank}, booktitle = {Proceedings of the fifth ACM conference on Recommender systems}, publisher = {ACM}, address = {New York, NY, USA}, year = {2011}, pages = {45--52}, url = {http://doi.acm.org/10.1145/2043932.2043945}, doi = {10.1145/2043932.2043945}, isbn = {978-1-4503-0683-6}, keywords = {bookmarking, collaborative, folkrank, folksonomy, ranking, search, tagging, web, pagerank}, abstract = {This paper looks inside FolkRank, one of the well-known folksonomy-based algorithms, to present its fundamental properties and promising possibilities for improving performance in tag recommendations. Moreover, we introduce a new way to compute a differential approach in FolkRank by representing it as a linear combination of the personalized PageRank vectors. By the linear combination, we present FolkRank's probabilistic interpretation that grasps how FolkRank works on a folksonomy graph in terms of the random surfer model. We also propose new FolkRank-like methods for tag recommendations to efficiently compute tags' rankings and thus reduce expensive computational cost of FolkRank. We show that the FolkRank approaches are feasible to recommend tags in real-time scenarios as well. The experimental evaluations show that the proposed methods provide fast tag recommendations with reasonable quality, as compared to FolkRank. Additionally, we discuss the diversity of the top n tags recommended by FolkRank and its variants.} }

%0 = inproceedings %A = Kim, Heung-Nam and El Saddik, Abdulmotaleb %B = Proceedings of the fifth ACM conference on Recommender systems %C = New York, NY, USA %D = 2011 %I = ACM %T = Personalized PageRank vectors for tag recommendations: inside FolkRank %U = http://doi.acm.org/10.1145/2043932.2043945

This article describes a framework that captures collaborative tagging systems, and derives from it an overview of user tasks that qualify for personalization in such a system. Major research areas have focused on some of these tasks, but we identify many more opportunities. We propose a collaborative model that combines collaborative filtering and information retrieval techniques in order to assists the user to achieve these tasks. Based only on the user's tags, this personalization model assumes that a user's tags identify this user's taste. Because many users do not only tag the content that matches their taste, we propose an evaluating experiment that shows if rating information can be used to adjust the users' taste profiles. This experiment is one of the steps to advance to a completely personalized model, integrating user preference, content annotations and people relations.

@inproceedings{clements2007personalization, author = {Clements, M.}, title = {Personalization of Social Media}, booktitle = {Proceedings of BCS IRSG Symposium: Future Directions in Information Access 2007}, year = {2007}, keywords = {collaborative, folksonomy, media, personalization, recommender, social, tagging}, abstract = {This article describes a framework that captures collaborative tagging systems, and derives from it an overview of user tasks that qualify for personalization in such a system. Major research areas have focused on some of these tasks, but we identify many more opportunities. We propose a collaborative model that combines collaborative filtering and information retrieval techniques in order to assists the user to achieve these tasks. Based only on the user's tags, this personalization model assumes that a user's tags identify this user's taste. Because many users do not only tag the content that matches their taste, we propose an evaluating experiment that shows if rating information can be used to adjust the users' taste profiles. This experiment is one of the steps to advance to a completely personalized model, integrating user preference, content annotations and people relations.} }

%0 = inproceedings %A = Clements, M. %B = Proceedings of BCS IRSG Symposium: Future Directions in Information Access 2007 %D = 2007 %T = Personalization of Social Media

@inproceedings{heckner2009personal, author = {Heckner, Markus and Heilemann, Michael and Wolff, Christian}, title = {Personal Information Management vs. Resource Sharing: Towards a Model of Information Behaviour in Social Tagging Systems}, booktitle = {Int'l AAAI Conference on Weblogs and Social Media (ICWSM)}, address = {San Jose, CA, USA}, year = {2009}, keywords = {bibsonomy, folksonomy, information, management, motivation, social, tagging, toread} }

%0 = inproceedings %A = Heckner, Markus and Heilemann, Michael and Wolff, Christian %B = Int'l AAAI Conference on Weblogs and Social Media (ICWSM) %C = San Jose, CA, USA %D = 2009 %T = Personal Information Management vs. Resource Sharing: Towards a Model of Information Behaviour in Social Tagging Systems

Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning.</p> <p>In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation.

@inproceedings{rendle2010pairwise, author = {Rendle, Steffen and Schmidt-Thieme, Lars}, title = {Pairwise interaction tensor factorization for personalized tag recommendation}, booktitle = {Proceedings of the third ACM international conference on Web search and data mining}, publisher = {ACM}, address = {New York, NY, USA}, year = {2010}, pages = {81--90}, url = {http://doi.acm.org/10.1145/1718487.1718498}, doi = {10.1145/1718487.1718498}, isbn = {978-1-60558-889-6}, keywords = {collaborative, factorization, folksonomy, personalization, recommender, tag, tagging, tensor}, abstract = {Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning.</p> <p>In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation.} }

%0 = inproceedings %A = Rendle, Steffen and Schmidt-Thieme, Lars %B = Proceedings of the third ACM international conference on Web search and data mining %C = New York, NY, USA %D = 2010 %I = ACM %T = Pairwise interaction tensor factorization for personalized tag recommendation %U = http://doi.acm.org/10.1145/1718487.1718498

Social network systems, like last.fm, play a significant role in Web 2.0, containing large amounts of multimedia-enriched data that are enhanced both by explicit user-provided annotations and implicit aggregated feedback describing the personal preferences of each user. It is also a common tendency for these systems to encourage the creation of virtual networks among their users by allowing them to establish bonds of friendship and thus provide a novel and direct medium for the exchange of data.
We investigate the role of these additional relationships in developing a track recommendation system. Taking into account both the social annotation and friendships inherent in the social graph established among users, items and tags, we created a collaborative recommendation system that effectively adapts to the personal information needs of each user. We adopt the generic framework of Random Walk with Restarts in order to provide with a more natural and efficient way to represent social networks.
In this work we collected a representative enough portion of the music social network last.fm, capturing explicitly expressed bonds of friendship of the user as well as social tags. We performed a series of comparison experiments between the Random Walk with Restarts model and a user-based collaborative filtering method using the Pearson Correlation similarity. The results show that the graph model system benefits from the additional information embedded in social knowledge. In addition, the graph model outperforms the standard collaborative filtering method.

@inproceedings{konstas2009social, author = {Konstas, Ioannis and Stathopoulos, Vassilios and Jose, Joemon M.}, title = {On social networks and collaborative recommendation}, booktitle = {Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval}, series = {SIGIR '09}, publisher = {ACM}, address = {New York, NY, USA}, year = {2009}, pages = {195--202}, url = {http://doi.acm.org/10.1145/1571941.1571977}, doi = {10.1145/1571941.1571977}, isbn = {978-1-60558-483-6}, keywords = {collaborative, folksonomy, random, recommender, tagging, walk}, abstract = {Social network systems, like last.fm, play a significant role in Web 2.0, containing large amounts of multimedia-enriched data that are enhanced both by explicit user-provided annotations and implicit aggregated feedback describing the personal preferences of each user. It is also a common tendency for these systems to encourage the creation of virtual networks among their users by allowing them to establish bonds of friendship and thus provide a novel and direct medium for the exchange of data.
We investigate the role of these additional relationships in developing a track recommendation system. Taking into account both the social annotation and friendships inherent in the social graph established among users, items and tags, we created a collaborative recommendation system that effectively adapts to the personal information needs of each user. We adopt the generic framework of Random Walk with Restarts in order to provide with a more natural and efficient way to represent social networks.
In this work we collected a representative enough portion of the music social network last.fm, capturing explicitly expressed bonds of friendship of the user as well as social tags. We performed a series of comparison experiments between the Random Walk with Restarts model and a user-based collaborative filtering method using the Pearson Correlation similarity. The results show that the graph model system benefits from the additional information embedded in social knowledge. In addition, the graph model outperforms the standard collaborative filtering method.} }

%0 = inproceedings %A = Konstas, Ioannis and Stathopoulos, Vassilios and Jose, Joemon M. %B = Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval %C = New York, NY, USA %D = 2009 %I = ACM %T = On social networks and collaborative recommendation %U = http://doi.acm.org/10.1145/1571941.1571977

Social tagging systems have established themselves as an important part in
day's web and have attracted the interest from our research community in a
riety of investigations. The overall vision of our community is that simply
rough interactions with the system, i.e., through tagging and sharing of
sources, users would contribute to building useful semantic structures as
ll as resource indexes using uncontrolled vocabulary not only due to the
sy-to-use mechanics. Henceforth, a variety of assumptions about social
gging systems have emerged, yet testing them has been difficult due to the
sence of suitable data. In this work we thoroughly investigate three
ailable assumptions - e.g., is a tagging system really social? - by examining
ve log data gathered from the real-world public social tagging system
bSonomy. Our empirical results indicate that while some of these assumptions
ld to a certain extent, other assumptions need to be reflected and viewed in
very critical light. Our observations have implications for the design of
ture search and other algorithms to better reflect the actual user behavior.

@techreport{doerfel2014course, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, title = {Of course we share! Testing Assumptions about Social Tagging Systems}, year = {2014}, note = {cite arxiv:1401.0629}, url = {http://arxiv.org/abs/1401.0629}, keywords = {2014, analysis, assumptions, bibsonomy, data, folksonomy, log, myown, share, social, tagging, testing, weblog}, abstract = {Social tagging systems have established themselves as an important part in
day's web and have attracted the interest from our research community in a
riety of investigations. The overall vision of our community is that simply
rough interactions with the system, i.e., through tagging and sharing of
sources, users would contribute to building useful semantic structures as
ll as resource indexes using uncontrolled vocabulary not only due to the
sy-to-use mechanics. Henceforth, a variety of assumptions about social
gging systems have emerged, yet testing them has been difficult due to the
sence of suitable data. In this work we thoroughly investigate three
ailable assumptions - e.g., is a tagging system really social? - by examining
ve log data gathered from the real-world public social tagging system
bSonomy. Our empirical results indicate that while some of these assumptions
ld to a certain extent, other assumptions need to be reflected and viewed in
very critical light. Our observations have implications for the design of
ture search and other algorithms to better reflect the actual user behavior.} }

%0 = techreport %A = Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus %D = 2014 %T = Of course we share! Testing Assumptions about Social Tagging Systems %U = http://arxiv.org/abs/1401.0629

@inproceedings{schmitz07network, author = {Schmitz, Christoph and Grahl, Miranda and Hotho, Andreas and Stumme, Gerd and Catutto, Ciro and Baldassarri, Andrea and Loreto, Vittorio and Servedio, Vito D. P.}, title = {Network Properties of Folksonomies}, booktitle = {Proc. WWW2007 Workshop ``Tagging and Metadata for Social Information Organization''}, address = {Banff}, year = {2007}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2007/schmitz07network.pdf}, keywords = {2007, emergent, fca, folksonomy, folksononomies, itegpub, l3s, myown, semantics, smallworld, sna, socialnetwork} }

%0 = inproceedings %A = Schmitz, Christoph and Grahl, Miranda and Hotho, Andreas and Stumme, Gerd and Catutto, Ciro and Baldassarri, Andrea and Loreto, Vittorio and Servedio, Vito D. P. %B = Proc. WWW2007 Workshop ``Tagging and Metadata for Social Information Organization'' %C = Banff %D = 2007 %T = Network Properties of Folksonomies %U = http://www.kde.cs.uni-kassel.de/stumme/papers/2007/schmitz07network.pdf

@article{cattuto2007networkb, author = {Cattuto, Ciro and Schmitz, Christoph and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio and Hotho, Andreas and Grahl, Miranda and Stumme, Gerd}, title = {Network Properties of Folksonomies}, editor = {Hoche, Susanne and Nürnberger, Andreas and Flach, Jürgen}, journal = {AI Communications Journal, Special Issue on ``Network Analysis in Natural Sciences and Engineering''}, publisher = {IOS Press}, year = {2007}, volume = {20}, number = {4}, pages = {245-262}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2007/cattuto2007network.pdf}, issn = {0921-7126}, keywords = {2007, emergent, fca, folksonomies, folksonomy, itegpub, l3s, myown, network, semantics, tagorapub} }

%0 = article %A = Cattuto, Ciro and Schmitz, Christoph and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio and Hotho, Andreas and Grahl, Miranda and Stumme, Gerd %D = 2007 %I = IOS Press %T = Network Properties of Folksonomies %U = http://www.kde.cs.uni-kassel.de/stumme/papers/2007/cattuto2007network.pdf

@article{cattuto2007networkb, author = {Cattuto, Ciro and Schmitz, Christoph and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio and Hotho, Andreas and Grahl, Miranda and Stumme, Gerd}, title = {Network Properties of Folksonomies}, editor = {Hoche, Susanne and Nürnberger, Andreas and Flach, Jürgen}, journal = {AI Communications Journal, Special Issue on ``Network Analysis in Natural Sciences and Engineering''}, publisher = {IOS Press}, year = {2007}, volume = {20}, number = {4}, pages = {245-262}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2007/cattuto2007network.pdf}, issn = {0921-7126}, keywords = {2007, semantics, emergent, folksonomy, tagorapub, folksonomies, l3s, network, itegpub, fca} }

%0 = article %A = Cattuto, Ciro and Schmitz, Christoph and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio and Hotho, Andreas and Grahl, Miranda and Stumme, Gerd %D = 2007 %I = IOS Press %T = Network Properties of Folksonomies %U = http://www.kde.cs.uni-kassel.de/stumme/papers/2007/cattuto2007network.pdf

@inproceedings{schmitz2006mining, author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, title = {Mining Association Rules in Folksonomies}, editor = {Batagelj, V. and Bock, H.-H. and Ferligoj, A. and vZiberna, A.}, booktitle = {Data Science and Classification: Proc. of the 10th IFCS Conf.}, series = {Studies in Classification, Data Analysis, and Knowledge Organization}, publisher = {Springer}, address = {Berlin, Heidelberg}, year = {2006}, pages = {261--270}, keywords = {2006, FCA, OntologyHandbook, association, folksonomy, itegpub, myown, rule} }

%0 = inproceedings %A = Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd %B = Data Science and Classification: Proc. of the 10th IFCS Conf. %C = Berlin, Heidelberg %D = 2006 %I = Springer %T = Mining Association Rules in Folksonomies

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.

@inproceedings{schmitz2006mining, author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, title = {Mining Association Rules in Folksonomies}, editor = {Batagelj, V. and Bock, H.-H. and Ferligoj, A. and �iberna, A.}, booktitle = {Data Science and Classification. Proceedings of the 10th IFCS Conf.}, series = {Studies in Classification, Data Analysis, and Knowledge Organization}, publisher = {Springer}, address = {Heidelberg}, year = {2006}, pages = {261--270}, keywords = {analysis, closely_related, diploma_thesis, folksonomy, nepomuk, network, semantic, ol_web2.0, methods_concepts, methods_concepthierarchy}, abstract = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.} }

%0 = inproceedings %A = Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd %B = Data Science and Classification. Proceedings of the 10th IFCS Conf. %C = Heidelberg %D = 2006 %I = Springer %T = Mining Association Rules in Folksonomies

In this paper we give a brief overview of different metadata mechanisms (like ontologies and folksonomies) and how they relate to each other. We identify major strengths and weaknesses of these mechanisms. We claim that these mechanisms can be classified from restricted (e.g., ontology) to free (e.g., free text tagging). In our view, these mechanisms should not be used in isolation, but rather as complementary solutions, in a continuous process wherein the strong points of one increase the semantic depth of the other. We give an overview of early active research already going on in this direction and propose that methodologies to support this process be developed. We demonstrate a possible approach, in which we mix tagging, taxonomy and ontology.

@inproceedings{christiaens2006metadata, author = {Christiaens, Stijn}, title = {Metadata Mechanisms: From Ontology to Folksonomy ... and Back}, booktitle = {Lecture Notes in Computer Science: On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops}, publisher = {Springer}, year = {2006}, url = {http://www.springerlink.com/content/m370107220473394}, keywords = {diploma_thesis, faceted_classification, folksonomy, folksonomy_background, ontology, semantic_web, tagging, ol_web2.0, background, widely_related}, abstract = {In this paper we give a brief overview of different metadata mechanisms (like ontologies and folksonomies) and how they relate to each other. We identify major strengths and weaknesses of these mechanisms. We claim that these mechanisms can be classified from restricted (e.g., ontology) to free (e.g., free text tagging). In our view, these mechanisms should not be used in isolation, but rather as complementary solutions, in a continuous process wherein the strong points of one increase the semantic depth of the other. We give an overview of early active research already going on in this direction and propose that methodologies to support this process be developed. We demonstrate a possible approach, in which we mix tagging, taxonomy and ontology.} }

%0 = inproceedings %A = Christiaens, Stijn %B = Lecture Notes in Computer Science: On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops %D = 2006 %I = Springer %T = Metadata Mechanisms: From Ontology to Folksonomy ... and Back %U = http://www.springerlink.com/content/m370107220473394

@inproceedings{Laniado2010, author = {Laniado, David and Mika, Peter}, title = {Making Sense of Twitter.}, editor = {Patel-Schneider, Peter F. and Pan, Yue and Hitzler, Pascal and Mika, Peter and Zhang, Lei and Pan, Jeff Z. and Horrocks, Ian and Glimm, Birte}, booktitle = {International Semantic Web Conference (1)}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, year = {2010}, volume = {6496}, pages = {470-485}, url = {http://dblp.uni-trier.de/db/conf/semweb/iswc2010-1.html#LaniadoM10}, isbn = {978-3-642-17745-3}, keywords = {analysis, folksonomy, tagging, toread, twitter} }

%0 = inproceedings %A = Laniado, David and Mika, Peter %B = International Semantic Web Conference (1) %D = 2010 %I = Springer %T = Making Sense of Twitter. %U = http://dblp.uni-trier.de/db/conf/semweb/iswc2010-1.html#LaniadoM10

In social bookmarking systems users describe bookmarksby keywords called tags. The structure behindthese social systems, called folksonomies, can beviewed as a tripartite hypergraph of user, tag and resourcenodes. This underlying network shows specificstructural properties that explain its growth and the possibilityof serendipitous exploration.Search engines filter the vast information of the web.Queries describe a user’s information need. In responseto the displayed results of the search engine, users clickon the links of the result page as they expect the answerto be of relevance. The clickdata can be represented as afolksonomy in which queries are descriptions of clickedURLs. This poster analyzes the topological characteristicsof the resulting tripartite hypergraph of queries,users and bookmarks of two query logs and compares ittwo a snapshot of the folksonomy del.icio.us.

@inproceedings{jaeschke2008logsonomy, author = {Jäschke, Robert and Krause, Beate and Hotho, Andreas and Stumme, Gerd}, title = {Logsonomy -- A Search Engine Folksonomy}, booktitle = {Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)}, publisher = {AAAI Press}, year = {2008}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf}, keywords = {2008, engine, folksonomies, folksonomy, itegpub, logsonomies, logsonomy, myown, search, tagorapub}, abstract = {In social bookmarking systems users describe bookmarksby keywords called tags. The structure behindthese social systems, called folksonomies, can beviewed as a tripartite hypergraph of user, tag and resourcenodes. This underlying network shows specificstructural properties that explain its growth and the possibilityof serendipitous exploration.Search engines filter the vast information of the web.Queries describe a user’s information need. In responseto the displayed results of the search engine, users clickon the links of the result page as they expect the answerto be of relevance. The clickdata can be represented as afolksonomy in which queries are descriptions of clickedURLs. This poster analyzes the topological characteristicsof the resulting tripartite hypergraph of queries,users and bookmarks of two query logs and compares ittwo a snapshot of the folksonomy del.icio.us.} }

%0 = inproceedings %A = Jäschke, Robert and Krause, Beate and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008) %D = 2008 %I = AAAI Press %T = Logsonomy -- A Search Engine Folksonomy %U = http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf

In social bookmarking systems users describe bookmarksby keywords called tags. The structure behindthese social systems, called folksonomies, can beviewed as a tripartite hypergraph of user, tag and resourcenodes. This underlying network shows specificstructural properties that explain its growth and the possibilityof serendipitous exploration.Search engines filter the vast information of the web.Queries describe a user’s information need. In responseto the displayed results of the search engine, users clickon the links of the result page as they expect the answerto be of relevance. The clickdata can be represented as afolksonomy in which queries are descriptions of clickedURLs. This poster analyzes the topological characteristicsof the resulting tripartite hypergraph of queries,users and bookmarks of two query logs and compares ittwo a snapshot of the folksonomy del.icio.us.

@inproceedings{jaeschke2008logsonomy, author = {Jäschke, Robert and Krause, Beate and Hotho, Andreas and Stumme, Gerd}, title = {Logsonomy -- A Search Engine Folksonomy}, booktitle = {Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)}, publisher = {AAAI Press}, year = {2008}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf}, keywords = {2008, search, engine, logsonomy, logsonomies, folksonomy, tagorapub, folksonomies, itegpub}, abstract = {In social bookmarking systems users describe bookmarksby keywords called tags. The structure behindthese social systems, called folksonomies, can beviewed as a tripartite hypergraph of user, tag and resourcenodes. This underlying network shows specificstructural properties that explain its growth and the possibilityof serendipitous exploration.Search engines filter the vast information of the web.Queries describe a user’s information need. In responseto the displayed results of the search engine, users clickon the links of the result page as they expect the answerto be of relevance. The clickdata can be represented as afolksonomy in which queries are descriptions of clickedURLs. This poster analyzes the topological characteristicsof the resulting tripartite hypergraph of queries,users and bookmarks of two query logs and compares ittwo a snapshot of the folksonomy del.icio.us.} }

%0 = inproceedings %A = Jäschke, Robert and Krause, Beate and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008) %D = 2008 %I = AAAI Press %T = Logsonomy -- A Search Engine Folksonomy %U = http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf

Social bookmarking systems constitute an established
rt of the Web 2.0. In such systems
ers describe bookmarks by keywords
lled tags. The structure behind these social
stems, called folksonomies, can be viewed
a tripartite hypergraph of user, tag and resource
des. This underlying network shows
ecific structural properties that explain its
owth and the possibility of serendipitous
ploration.
day’s search engines represent the gateway
retrieve information from the World Wide
b. Short queries typically consisting of
o to three words describe a user’s information
ed. In response to the displayed
sults of the search engine, users click on
e links of the result page as they expect
e answer to be of relevance.
is clickdata can be represented as a folksonomy
which queries are descriptions of
icked URLs. The resulting network structure,
ich we will term logsonomy is very
milar to the one of folksonomies. In order
find out about its properties, we analyze
e topological characteristics of the tripartite
pergraph of queries, users and bookmarks
a large snapshot of del.icio.us and
query logs of two large search engines.
l of the three datasets show small world
operties. The tagging behavior of users,
ich is explained by preferential attachment
the tags in social bookmark systems, is
flected in the distribution of single query
rds in search engines. We can conclude
at the clicking behaviour of search engine
ers based on the displayed search results
d the tagging behaviour of social bookmarking
ers is driven by similar dynamics.

@inproceedings{krause2008logsonomy, author = {Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd}, title = {Logsonomy - Social Information Retrieval with Logdata}, booktitle = {HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia}, publisher = {ACM}, address = {New York, NY, USA}, year = {2008}, pages = {157--166}, url = {http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia}, doi = {http://doi.acm.org/10.1145/1379092.1379123}, isbn = {978-1-59593-985-2}, keywords = {folksonomy, information, logsonomy, retrieval, social}, abstract = {Social bookmarking systems constitute an established
rt of the Web 2.0. In such systems
ers describe bookmarks by keywords
lled tags. The structure behind these social
stems, called folksonomies, can be viewed
a tripartite hypergraph of user, tag and resource
des. This underlying network shows
ecific structural properties that explain its
owth and the possibility of serendipitous
ploration.
day’s search engines represent the gateway
retrieve information from the World Wide
b. Short queries typically consisting of
o to three words describe a user’s information
ed. In response to the displayed
sults of the search engine, users click on
e links of the result page as they expect
e answer to be of relevance.
is clickdata can be represented as a folksonomy
which queries are descriptions of
icked URLs. The resulting network structure,
ich we will term logsonomy is very
milar to the one of folksonomies. In order
find out about its properties, we analyze
e topological characteristics of the tripartite
pergraph of queries, users and bookmarks
a large snapshot of del.icio.us and
query logs of two large search engines.
l of the three datasets show small world
operties. The tagging behavior of users,
ich is explained by preferential attachment
the tags in social bookmark systems, is
flected in the distribution of single query
rds in search engines. We can conclude
at the clicking behaviour of search engine
ers based on the displayed search results
d the tagging behaviour of social bookmarking
ers is driven by similar dynamics.} }

%0 = inproceedings %A = Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd %B = HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia %C = New York, NY, USA %D = 2008 %I = ACM %T = Logsonomy - Social Information Retrieval with Logdata %U = http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia

Social bookmarking systems constitute an established
rt of the Web 2.0. In such systems
ers describe bookmarks by keywords
lled tags. The structure behind these social
stems, called folksonomies, can be viewed
a tripartite hypergraph of user, tag and resource
des. This underlying network shows
ecific structural properties that explain its
owth and the possibility of serendipitous
ploration.
day’s search engines represent the gateway
retrieve information from the World Wide
b. Short queries typically consisting of
o to three words describe a user’s information
ed. In response to the displayed
sults of the search engine, users click on
e links of the result page as they expect
e answer to be of relevance.
is clickdata can be represented as a folksonomy
which queries are descriptions of
icked URLs. The resulting network structure,
ich we will term logsonomy is very
milar to the one of folksonomies. In order
find out about its properties, we analyze
e topological characteristics of the tripartite
pergraph of queries, users and bookmarks
a large snapshot of del.icio.us and
query logs of two large search engines.
l of the three datasets show small world
operties. The tagging behavior of users,
ich is explained by preferential attachment
the tags in social bookmark systems, is
flected in the distribution of single query
rds in search engines. We can conclude
at the clicking behaviour of search engine
ers based on the displayed search results
d the tagging behaviour of social bookmarking
ers is driven by similar dynamics.

@inproceedings{krause2008logsonomy, author = {Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd}, title = {Logsonomy - Social Information Retrieval with Logdata}, booktitle = {HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia}, publisher = {ACM}, address = {New York, NY, USA}, year = {2008}, pages = {157--166}, url = {http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia}, doi = {http://doi.acm.org/10.1145/1379092.1379123}, isbn = {978-1-59593-985-2}, keywords = {2.0, 2008, analysis, folksonomy, information, itegpub, logsonomy, myown, network, retrieval, search, social, tagorapub, web, web2.0, web20}, abstract = {Social bookmarking systems constitute an established
rt of the Web 2.0. In such systems
ers describe bookmarks by keywords
lled tags. The structure behind these social
stems, called folksonomies, can be viewed
a tripartite hypergraph of user, tag and resource
des. This underlying network shows
ecific structural properties that explain its
owth and the possibility of serendipitous
ploration.
day’s search engines represent the gateway
retrieve information from the World Wide
b. Short queries typically consisting of
o to three words describe a user’s information
ed. In response to the displayed
sults of the search engine, users click on
e links of the result page as they expect
e answer to be of relevance.
is clickdata can be represented as a folksonomy
which queries are descriptions of
icked URLs. The resulting network structure,
ich we will term logsonomy is very
milar to the one of folksonomies. In order
find out about its properties, we analyze
e topological characteristics of the tripartite
pergraph of queries, users and bookmarks
a large snapshot of del.icio.us and
query logs of two large search engines.
l of the three datasets show small world
operties. The tagging behavior of users,
ich is explained by preferential attachment
the tags in social bookmark systems, is
flected in the distribution of single query
rds in search engines. We can conclude
at the clicking behaviour of search engine
ers based on the displayed search results
d the tagging behaviour of social bookmarking
ers is driven by similar dynamics.} }

%0 = inproceedings %A = Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd %B = HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia %C = New York, NY, USA %D = 2008 %I = ACM %T = Logsonomy - Social Information Retrieval with Logdata %U = http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia

The objective of a tag recommendation system is to propose a set of tags for a resource to ease the tagging process done manually by a user. Tag recommendation is an interesting and well defined research problem. However, while solving it, it is easy to forget about its practical implications. We discuss the practical aspects of tag recommendation and propose a system that successfully addresses the problem of learning in tag recommendation, without sacrificing efficiency. Learning is realized in two aspects: adaptation to newly added posts and parameter tuning. The content of each added post is used to update the resource and user profiles as well as associations between tags. Parameter tuning allows the system to automatically adjust the way tag sources (e.g., content related tags or user profile tags) are combined to match the characteristics of a specific collaborative tagging system. The evaluation on data from three collaborative tagging systems confirmed the importance of both learning methods. Finally, an architecture based on text indexing makes the system efficient enough to serve in real time collaborative tagging systems with number of posts counted in millions, given limited computing resources.

@inproceedings{lipczak2010learning, author = {Lipczak, Marek and Milios, Evangelos}, title = {Learning in efficient tag recommendation}, booktitle = {Proceedings of the fourth ACM conference on Recommender systems}, series = {RecSys '10}, publisher = {ACM}, address = {New York, NY, USA}, year = {2010}, pages = {167--174}, url = {http://doi.acm.org/10.1145/1864708.1864741}, doi = {10.1145/1864708.1864741}, isbn = {978-1-60558-906-0}, keywords = {2010, collaborative, folksonomy, recommender, tagging}, abstract = {The objective of a tag recommendation system is to propose a set of tags for a resource to ease the tagging process done manually by a user. Tag recommendation is an interesting and well defined research problem. However, while solving it, it is easy to forget about its practical implications. We discuss the practical aspects of tag recommendation and propose a system that successfully addresses the problem of learning in tag recommendation, without sacrificing efficiency. Learning is realized in two aspects: adaptation to newly added posts and parameter tuning. The content of each added post is used to update the resource and user profiles as well as associations between tags. Parameter tuning allows the system to automatically adjust the way tag sources (e.g., content related tags or user profile tags) are combined to match the characteristics of a specific collaborative tagging system. The evaluation on data from three collaborative tagging systems confirmed the importance of both learning methods. Finally, an architecture based on text indexing makes the system efficient enough to serve in real time collaborative tagging systems with number of posts counted in millions, given limited computing resources.} }

%0 = inproceedings %A = Lipczak, Marek and Milios, Evangelos %B = Proceedings of the fourth ACM conference on Recommender systems %C = New York, NY, USA %D = 2010 %I = ACM %T = Learning in efficient tag recommendation %U = http://doi.acm.org/10.1145/1864708.1864741

@article{cimiano05learning, author = {Cimiano, Philipp and Hotho, Andreas and Staab, Steffen}, title = {Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis}, journal = {Journal on Artificial Intelligence Research}, year = {2005}, volume = {24}, pages = {305-339}, url = {http://dblp.uni-trier.de/db/journals/jair/jair24.html#CimianoHS05}, keywords = {2005, fca, folksonomy, hierarchies, hierarchy, learning, myown, ontologies, text} }

%0 = article %A = Cimiano, Philipp and Hotho, Andreas and Staab, Steffen %D = 2005 %T = Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis %U = http://dblp.uni-trier.de/db/journals/jair/jair24.html#CimianoHS05

Kollaborative Verschlagwortungssysteme bieten Nutzern die Möglichkeit zur freien Verschlagwortung von Ressourcen im World Wide Web. Sie ermöglichen dem Nutzer beliebige Ressourcen mit frei wählbaren Schlagwörtern – so genannten Tags – zu versehen (Social Tagging). Im weiteren Sinne ist Social Tagging nichts anderes als das Indexieren von Ressourcen durch die Nutzenden selbst. Dabei sind die Tag-Zuordnungen für den einzelnen Nutzer und für die gesamte Community in vielerlei Hinsicht hilfreich. So können durch Tags persönliche Ideen oder Wertungen für eine Ressource ausgedrückt werden. Außerdem können Tags als Kommunikationsmittel von den Nutzern oder Nutzergruppen untereinander verwendet werden. Tags helfen zudem bei der Navigation, beim Suchen und beim zufälligen Entdecken von neuen Ressourcen. Das Verschlagworten der Ressourcen ist für unbedarfte Anwender eine kognitiv anspruchsvolle Aufgabe. Als Unterstützung können Tag-Recommender eingesetzt werden, die Nutzern passende Tags vorschlagen sollen.
UniVideo ist das Videoportal der Universität Kassel, das jedem Mitglied der Hochschule ermöglicht Videos bereitzustellen und weltweit über das WWW abrufbar zu machen. Die bereitgestellten Videos müssen von ihren Eigentümern beim Hochladen verschlagwortet werden. Die dadurch entstehende Struktur dient wiederum als Grundlage für die Navigation in UniVideo. In dieser Arbeit werden vier verschiedene Ansätze für Tag-Recommender theoretisch diskutiert und deren praktische Umsetzung für UniVideo untersucht und bewertet. Dabei werden zunächst die Grundlagen des Social Taggings erläutert und der Aufbau von UniVideo erklärt, bevor die Umsetzung der vier einzelnen Tag-Recommender beschrieben wird. Anschließend wird gezeigt wie aus den einzelnen Tag-Recommendern durch Verschmelzung ein hybrider Tag-Recommender umgesetzt werden kann.

@mastersthesis{bottger2012konzept, author = {Böttger, Sebastian}, title = {Konzept und Umsetzung eines Tag-Recommenders für Video-Ressourcen am Beispiel UniVideo}, school = {Universität Kassel}, type = {Bachelor Thesis}, address = {Kassel}, year = {2012}, url = {http://www.uni-kassel.de/~seboettg/ba-thesis.pdf}, keywords = {ba-thesis, bathesis, folksonomy, myown, recommender, tagging, tagrecommendation, thesis, univideo, video}, abstract = {Kollaborative Verschlagwortungssysteme bieten Nutzern die Möglichkeit zur freien Verschlagwortung von Ressourcen im World Wide Web. Sie ermöglichen dem Nutzer beliebige Ressourcen mit frei wählbaren Schlagwörtern – so genannten Tags – zu versehen (Social Tagging). Im weiteren Sinne ist Social Tagging nichts anderes als das Indexieren von Ressourcen durch die Nutzenden selbst. Dabei sind die Tag-Zuordnungen für den einzelnen Nutzer und für die gesamte Community in vielerlei Hinsicht hilfreich. So können durch Tags persönliche Ideen oder Wertungen für eine Ressource ausgedrückt werden. Außerdem können Tags als Kommunikationsmittel von den Nutzern oder Nutzergruppen untereinander verwendet werden. Tags helfen zudem bei der Navigation, beim Suchen und beim zufälligen Entdecken von neuen Ressourcen. Das Verschlagworten der Ressourcen ist für unbedarfte Anwender eine kognitiv anspruchsvolle Aufgabe. Als Unterstützung können Tag-Recommender eingesetzt werden, die Nutzern passende Tags vorschlagen sollen.
UniVideo ist das Videoportal der Universität Kassel, das jedem Mitglied der Hochschule ermöglicht Videos bereitzustellen und weltweit über das WWW abrufbar zu machen. Die bereitgestellten Videos müssen von ihren Eigentümern beim Hochladen verschlagwortet werden. Die dadurch entstehende Struktur dient wiederum als Grundlage für die Navigation in UniVideo. In dieser Arbeit werden vier verschiedene Ansätze für Tag-Recommender theoretisch diskutiert und deren praktische Umsetzung für UniVideo untersucht und bewertet. Dabei werden zunächst die Grundlagen des Social Taggings erläutert und der Aufbau von UniVideo erklärt, bevor die Umsetzung der vier einzelnen Tag-Recommender beschrieben wird. Anschließend wird gezeigt wie aus den einzelnen Tag-Recommendern durch Verschmelzung ein hybrider Tag-Recommender umgesetzt werden kann.} }

%0 = mastersthesis %A = Böttger, Sebastian %C = Kassel %D = 2012 %T = Konzept und Umsetzung eines Tag-Recommenders für Video-Ressourcen am Beispiel UniVideo %U = http://www.uni-kassel.de/~seboettg/ba-thesis.pdf

@inproceedings{hotho2006information, author = {Hotho, Andreas and J?schke, Robert and Schmitz, Christoph and Stumme, Gerd}, title = {Information Retrieval in Folksonomies: Search and Ranking}, editor = {Sure, York and Domingue, John}, booktitle = {The Semantic Web: Research and Applications}, series = {LNAI}, publisher = {Springer}, address = {Heidelberg}, year = {2006}, volume = {4011}, pages = {411-426}, keywords = {2006, FCA, IR, OntologyHandbook, folkrank, folksonomy, information, informationretrieval, itegpub, mimose, myown, pagerank, ranking, retrieval} }

%0 = inproceedings %A = Hotho, Andreas and J?schke, Robert and Schmitz, Christoph and Stumme, Gerd %B = The Semantic Web: Research and Applications %C = Heidelberg %D = 2006 %I = Springer %T = Information Retrieval in Folksonomies: Search and Ranking

@inproceedings{hotho2006information, author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, title = {Information Retrieval in Folksonomies: Search and Ranking}, editor = {Sure, York and Domingue, John}, booktitle = {The Semantic Web: Research and Applications}, series = {LNAI}, publisher = {Springer}, address = {Heidelberg}, year = {2006}, volume = {4011}, pages = {411-426}, keywords = {2006, FCA, IR, OntologyHandbook, folkrank, folksonomy, information, informationretrieval, itegpub, mimose, myown, pagerank, ranking, retrieval} }

%0 = inproceedings %A = Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd %B = The Semantic Web: Research and Applications %C = Heidelberg %D = 2006 %I = Springer %T = Information Retrieval in Folksonomies: Search and Ranking

@inproceedings{abrams1998information, author = {Abrams, David and Baecker, Ron and Chignell, Mark}, title = {Information archiving with bookmarks: personal Web space construction and organization}, booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems}, series = {CHI '98}, publisher = {ACM Press/Addison-Wesley Publishing Co.}, address = {New York, NY, USA}, year = {1998}, pages = {41--48}, url = {http://dx.doi.org/10.1145/274644.274651}, doi = {10.1145/274644.274651}, isbn = {0-201-30987-4}, keywords = {analysis, bookmarking, folksonomy, log, social, usage} }

%0 = inproceedings %A = Abrams, David and Baecker, Ron and Chignell, Mark %B = Proceedings of the SIGCHI Conference on Human Factors in Computing Systems %C = New York, NY, USA %D = 1998 %I = ACM Press/Addison-Wesley Publishing Co. %T = Information archiving with bookmarks: personal Web space construction and organization %U = http://dx.doi.org/10.1145/274644.274651

Collaborative tagging has emerged as a mechanism to describe items in large on-line collections. Tags are assigned by users to describe and find back items, but it is also tempting to describe the users in terms of the tags they assign or in terms of the tags of the items they are interested in. The tag-based profile thus obtained can be used to recommend new items. If we recommend new items by computing their similarity to the user profile or to all items seen by the user, we run into the risk of recommending only neutral items that are a bit relevant for each topic a user is interested in. In order to increase user satisfaction many recommender systems not only optimize for accuracy but also for diversity. Often it is assumed that there exists a trade-off between accuracy and diversity. In this paper we introduce topic aware recommendation algorithms. Topic aware algorithms first detect different interests in the user profile and then generate recommendations for each of these interests. We study topic aware variants of three tag based recommendation algorithms and show that each of them gives better recommendations than their base variants, both in terms of precision and recall and in terms of diversity.

@incollection{wartena2011improving, author = {Wartena, Christian and Wibbels, Martin}, title = {Improving Tag-Based Recommendation by Topic Diversification}, editor = {Clough, Paul and Foley, Colum and Gurrin, Cathal and Jones, Gareth and Kraaij, Wessel and Lee, Hyowon and Mudoch, Vanessa}, booktitle = {Advances in Information Retrieval}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2011}, volume = {6611}, pages = {43--54}, url = {http://dx.doi.org/10.1007/978-3-642-20161-5_7}, doi = {10.1007/978-3-642-20161-5_7}, isbn = {978-3-642-20160-8}, keywords = {folksonomy, item, recommender, social, tagging, topic}, abstract = {Collaborative tagging has emerged as a mechanism to describe items in large on-line collections. Tags are assigned by users to describe and find back items, but it is also tempting to describe the users in terms of the tags they assign or in terms of the tags of the items they are interested in. The tag-based profile thus obtained can be used to recommend new items. If we recommend new items by computing their similarity to the user profile or to all items seen by the user, we run into the risk of recommending only neutral items that are a bit relevant for each topic a user is interested in. In order to increase user satisfaction many recommender systems not only optimize for accuracy but also for diversity. Often it is assumed that there exists a trade-off between accuracy and diversity. In this paper we introduce topic aware recommendation algorithms. Topic aware algorithms first detect different interests in the user profile and then generate recommendations for each of these interests. We study topic aware variants of three tag based recommendation algorithms and show that each of them gives better recommendations than their base variants, both in terms of precision and recall and in terms of diversity.} }

%0 = incollection %A = Wartena, Christian and Wibbels, Martin %B = Advances in Information Retrieval %C = Berlin/Heidelberg %D = 2011 %I = Springer %T = Improving Tag-Based Recommendation by Topic Diversification %U = http://dx.doi.org/10.1007/978-3-642-20161-5_7

Collaborative tagging services (folksonomies) have been among the stars of the Web 2.0 era. They allow their users to label diverse resources with freely chosen keywords (tags). Our studies of two real-world folksonomies unveil that individual users develop highly personalized vocabularies of tags. While these meet individual needs and preferences, the considerable differences between personal tag vocabularies (personomies) impede services such as social search or customized tag recommendation. In this paper, we introduce a novel user-centric tag model that allows us to derive mappings between personal tag vocabularies and the corresponding folksonomies. Using these mappings, we can infer the meaning of user-assigned tags and can predict choices of tags a user may want to assign to new items. Furthermore, our translational approach helps in reducing common problems related to tag ambiguity, synonymous tags, or multilingualism. We evaluate the applicability of our method in tag recommendation and tag-based social search. Extensive experiments show that our translational model improves the prediction accuracy in both scenarios.

@inproceedings{wetzker2010translating, author = {Wetzker, Robert and Zimmermann, Carsten and Bauckhage, Christian and Albayrak, Sahin}, title = {I tag, you tag: translating tags for advanced user models}, booktitle = {Proceedings of the third ACM international conference on Web search and data mining}, series = {WSDM '10}, publisher = {ACM}, address = {New York, NY, USA}, year = {2010}, pages = {71--80}, url = {http://doi.acm.org/10.1145/1718487.1718497}, doi = {10.1145/1718487.1718497}, isbn = {978-1-60558-889-6}, keywords = {folksonomy, model, recommender, tagging, taggingsurvey}, abstract = {Collaborative tagging services (folksonomies) have been among the stars of the Web 2.0 era. They allow their users to label diverse resources with freely chosen keywords (tags). Our studies of two real-world folksonomies unveil that individual users develop highly personalized vocabularies of tags. While these meet individual needs and preferences, the considerable differences between personal tag vocabularies (personomies) impede services such as social search or customized tag recommendation. In this paper, we introduce a novel user-centric tag model that allows us to derive mappings between personal tag vocabularies and the corresponding folksonomies. Using these mappings, we can infer the meaning of user-assigned tags and can predict choices of tags a user may want to assign to new items. Furthermore, our translational approach helps in reducing common problems related to tag ambiguity, synonymous tags, or multilingualism. We evaluate the applicability of our method in tag recommendation and tag-based social search. Extensive experiments show that our translational model improves the prediction accuracy in both scenarios.} }

%0 = inproceedings %A = Wetzker, Robert and Zimmermann, Carsten and Bauckhage, Christian and Albayrak, Sahin %B = Proceedings of the third ACM international conference on Web search and data mining %C = New York, NY, USA %D = 2010 %I = ACM %T = I tag, you tag: translating tags for advanced user models %U = http://doi.acm.org/10.1145/1718487.1718497

@article{10.1109/TKDE.2012.115, author = {Zubiaga, Arkaitz and Fresno, Victor and Martinez, Raquel and Garcia-Plaza, Alberto P.}, title = {Harnessing Folksonomies to Produce a Social Classification of Resources}, journal = {IEEE Transactions on Knowledge and Data Engineering}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, year = {2012}, volume = {99}, number = {PrePrints}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.115}, issn = {1041-4347}, keywords = {classification, delicious, folksonomy, tagging, toread, dataset} }

%0 = article %A = Zubiaga, Arkaitz and Fresno, Victor and Martinez, Raquel and Garcia-Plaza, Alberto P. %C = Los Alamitos, CA, USA %D = 2012 %I = IEEE Computer Society %T = Harnessing Folksonomies to Produce a Social Classification of Resources

FReSET is a new recommender systems evaluation framework aiming to support research on folksonomy-based recommender systems. It provides interfaces for the implementation of folksonomy-based recommender systems and supports the consistent and reproducible offline evaluations on historical data. Unlike other recommender systems framework projects, the emphasis here is on providing a flexible framework allowing users to implement their own folksonomy-based recommender algorithms and pre-processing filtering methods rather than just providing a collection of collaborative filtering implementations. FReSET includes a graphical interface for result visualization and different cross-validation implementations to complement the basic functionality.

@inproceedings{dominguezgarcia2012freset, author = {Domnguez Garca, Renato and Bender, Matthias and Anjorin, Mojisola and Rensing, Christoph and Steinmetz, Ralf}, title = {FReSET: an evaluation framework for folksonomy-based recommender systems}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, series = {RSWeb '12}, publisher = {ACM}, address = {New York, NY, USA}, year = {2012}, pages = {25--28}, url = {http://doi.acm.org/10.1145/2365934.2365939}, doi = {10.1145/2365934.2365939}, isbn = {978-1-4503-1638-5}, keywords = {evaluation, folksonomy, framework, freset}, abstract = {FReSET is a new recommender systems evaluation framework aiming to support research on folksonomy-based recommender systems. It provides interfaces for the implementation of folksonomy-based recommender systems and supports the consistent and reproducible offline evaluations on historical data. Unlike other recommender systems framework projects, the emphasis here is on providing a flexible framework allowing users to implement their own folksonomy-based recommender algorithms and pre-processing filtering methods rather than just providing a collection of collaborative filtering implementations. FReSET includes a graphical interface for result visualization and different cross-validation implementations to complement the basic functionality.} }

%0 = inproceedings %A = Domnguez Garca, Renato and Bender, Matthias and Anjorin, Mojisola and Rensing, Christoph and Steinmetz, Ralf %B = Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web %C = New York, NY, USA %D = 2012 %I = ACM %T = FReSET: an evaluation framework for folksonomy-based recommender systems %U = http://doi.acm.org/10.1145/2365934.2365939

@phdthesis{jaschke2011formal, author = {Jäschke, Robert}, title = {Formal concept analysis and tag recommendations in collaborative tagging systems}, series = {Dissertations in artificial intelligence}, publisher = {Akad. Verl.-Ges. AKA}, address = {Heidelberg}, year = {2011}, volume = {332}, url = {http://opac.bibliothek.uni-kassel.de/DB=1/PPN?PPN=231779038}, isbn = {1-60750-707-2}, keywords = {baarbeit, folksonomy, social, tagging, toread} }

%0 = phdthesis %A = Jäschke, Robert %C = Heidelberg %D = 2011 %I = Akad. Verl.-Ges. AKA %T = Formal concept analysis and tag recommendations in collaborative tagging systems %U = http://opac.bibliothek.uni-kassel.de/DB=1/PPN?PPN=231779038

@phdthesis{jaschke2011formal, author = {Jäschke, Robert}, title = {Formal concept analysis and tag recommendations in collaborative tagging systems}, series = {Dissertations in artificial intelligence}, publisher = {Akad. Verl.-Ges. AKA}, address = {Heidelberg}, year = {2011}, volume = {332}, url = {http://opac.bibliothek.uni-kassel.de/DB=1/PPN?PPN=231779038}, isbn = {1-60750-707-2}, keywords = {baarbeit, folksonomy, recommender, social_tagging, toread} }

%0 = phdthesis %A = Jäschke, Robert %C = Heidelberg %D = 2011 %I = Akad. Verl.-Ges. AKA %T = Formal concept analysis and tag recommendations in collaborative tagging systems %U = http://opac.bibliothek.uni-kassel.de/DB=1/PPN?PPN=231779038

@incollection{singer2014folksonomies, author = {Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, title = {Folksonomies}, booktitle = {Encyclopedia of Social Network Analysis and Mining}, publisher = {Springer}, year = {2014}, pages = {542--547}, keywords = {2014, characterization, folksonomy, myown} }

%0 = incollection %A = Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus %B = Encyclopedia of Social Network Analysis and Mining %D = 2014 %I = Springer %T = Folksonomies

@inproceedings{gwizdka2007finding, author = {Gwizdka, Jacek and Cole, Michael}, title = {Finding It on Google, Finding It on del.icio.us.}, editor = {Kovács, László and Fuhr, Norbert and Meghini, Carlo}, booktitle = {ECDL}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, year = {2007}, volume = {4675}, pages = {559-562}, url = {http://dblp.uni-trier.de/db/conf/ercimdl/ecdl2007.html#GwizdkaC07}, isbn = {978-3-540-74850-2}, keywords = {search, logsonomy, levsem09, folksonomy} }

%0 = inproceedings %A = Gwizdka, Jacek and Cole, Michael %B = ECDL %D = 2007 %I = Springer %T = Finding It on Google, Finding It on del.icio.us. %U = http://dblp.uni-trier.de/db/conf/ercimdl/ecdl2007.html#GwizdkaC07

Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.</p> <p>Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.

@inproceedings{Landia:2012:EFC:2365934.2365936, author = {Landia, Nikolas and Anand, Sarabjot Singh and Hotho, Andreas and Jäschke, Robert and Doerfel, Stephan and Mitzlaff, Folke}, title = {Extending FolkRank with content data}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, series = {RSWeb '12}, publisher = {ACM}, address = {New York, NY, USA}, year = {2012}, pages = {1--8}, url = {http://doi.acm.org/10.1145/2365934.2365936}, doi = {10.1145/2365934.2365936}, isbn = {978-1-4503-1638-5}, keywords = {2012, bookmarking, folkrank, folksonomy, myown, social, tagging}, abstract = {Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.</p> <p>Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.} }

%0 = inproceedings %A = Landia, Nikolas and Anand, Sarabjot Singh and Hotho, Andreas and Jäschke, Robert and Doerfel, Stephan and Mitzlaff, Folke %B = Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web %C = New York, NY, USA %D = 2012 %I = ACM %T = Extending FolkRank with content data %U = http://doi.acm.org/10.1145/2365934.2365936

@article{yu2011exploring, author = {Yu, L. and Li, Q. and Xie, H. and Cai, Y.}, title = {Exploring Folksonomy and Cooking Procedures to Boost Cooking Recipe Recommendation}, journal = {Web Technologies and Applications}, publisher = {Springer}, year = {2011}, pages = {119--130}, keywords = {application, cooking, folksonomy} }

%0 = article %A = Yu, L. and Li, Q. and Xie, H. and Cai, Y. %D = 2011 %I = Springer %T = Exploring Folksonomy and Cooking Procedures to Boost Cooking Recipe Recommendation

Motivated by the potential use of collaborative tagging systems to develop new recommender systems, we have implemented and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. On our first approach, Classic Collaborative filtering (CCF), we use Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. Our second approach, Neighbor-weighted Collaborative Filtering (NwCF), incorporates the amount of raters in the ranking formula of the recommendations. A modified version of the Okapi BM25 IR model over users ’ tags is implemented on our third approach to form the user neighborhood. Our results suggest that incorporating the number of raters into the algorithms leads to an improvement of precision, and they also support that tags can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors in a collaborative tagging system.

@inproceedings{parra2009evaluation, author = {Parra, Denis and Brusilovsky, Peter}, title = {Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike}, booktitle = {Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web}, series = {CEUR Workshop Proceedings}, year = {2009}, volume = {467}, url = {http://ceur-ws.org/Vol-467/paper5.pdf}, keywords = {collaborative, filtering, folksonomy, item, recommender, social, tagging}, abstract = {Motivated by the potential use of collaborative tagging systems to develop new recommender systems, we have implemented and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. On our first approach, Classic Collaborative filtering (CCF), we use Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. Our second approach, Neighbor-weighted Collaborative Filtering (NwCF), incorporates the amount of raters in the ranking formula of the recommendations. A modified version of the Okapi BM25 IR model over users ’ tags is implemented on our third approach to form the user neighborhood. Our results suggest that incorporating the number of raters into the algorithms leads to an improvement of precision, and they also support that tags can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors in a collaborative tagging system. } }

%0 = inproceedings %A = Parra, Denis and Brusilovsky, Peter %B = Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web %D = 2009 %T = Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike %U = http://ceur-ws.org/Vol-467/paper5.pdf

Social bookmark tools are rapidly emerging on the Web. In suchsystems users are setting up lightweight conceptual structurescalled folksonomies. The reason for their immediate success is thefact that no specific skills are needed for participating. In thispaper we specify a formal model for folksonomies, briefly describeour own system BibSonomy, which allows for sharing both bookmarks andpublication references, and discuss first steps towards emergent semantics.

@inproceedings{hotho2006emergent, author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, title = {Emergent Semantics in BibSonomy}, editor = {Hochberger, Christian and Liskowsky, Rüdiger}, booktitle = {Informatik 2006 -- Informatik für Menschen. Band 2}, series = {Lecture Notes in Informatics}, publisher = {Gesellschaft für Informatik}, address = {Bonn}, year = {2006}, volume = {P-94}, note = {Proc. Workshop on Applications of Semantic Technologies, Informatik 2006}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006emergent.pdf}, keywords = {2006, UniK, bibsonomy, emergence, emergent, emergentsemantics_evidence, folksonomy, hotho, itegpub, jaeschke, l3s, myown, nepomuk, ol_web2.0, schmitz, semantics, stumme, tagorapub}, abstract = {Social bookmark tools are rapidly emerging on the Web. In suchsystems users are setting up lightweight conceptual structurescalled folksonomies. The reason for their immediate success is thefact that no specific skills are needed for participating. In thispaper we specify a formal model for folksonomies, briefly describeour own system BibSonomy, which allows for sharing both bookmarks andpublication references, and discuss first steps towards emergent semantics.} }

%0 = inproceedings %A = Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd %B = Informatik 2006 -- Informatik für Menschen. Band 2 %C = Bonn %D = 2006 %I = Gesellschaft für Informatik %T = Emergent Semantics in BibSonomy %U = http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006emergent.pdf

The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.

@article{landia2013deeper, author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan}, title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations}, journal = {cs.IR}, year = {2013}, volume = {1310.1498}, url = {http://arxiv.org/abs/1310.1498}, keywords = {2013, bookmarking, collaborative, folkrank, folksonomy, graph, iteg, itegpub, l3s, recommender, social, tagging}, abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.} }

%0 = article %A = Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan %D = 2013 %T = Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations %U = http://arxiv.org/abs/1310.1498

The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.

@article{landia2013deeper, author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan}, title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations}, journal = {cs.IR}, year = {2013}, volume = {1310.1498}, url = {http://arxiv.org/abs/1310.1498}, keywords = {2013, bookmarking, collaborative, folkrank, folksonomy, graph, myown}, abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.} }

%0 = article %A = Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan %D = 2013 %T = Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations %U = http://arxiv.org/abs/1310.1498

The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.

@article{landia2013deeper, author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan}, title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations}, journal = {cs.IR}, year = {2013}, volume = {1310.1498}, url = {http://arxiv.org/abs/1310.1498}, keywords = {2013, bookmarking, collaborative, folkrank, folksonomy, graph, myown, recommender, social, tagging}, abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.} }

%0 = article %A = Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan %D = 2013 %T = Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations %U = http://arxiv.org/abs/1310.1498

@inproceedings{szomszor2008correlating, author = {Szomszor, Martin and Cantador, Iván and Alani, Harith}, title = {Correlating user profiles from multiple folksonomies}, booktitle = {Hypertext}, year = {2008}, pages = {33-42}, keywords = {matching, folksonomy, user_profiles, user, profile} }

%0 = inproceedings %A = Szomszor, Martin and Cantador, Iván and Alani, Harith %B = Hypertext %D = 2008 %T = Correlating user profiles from multiple folksonomies

Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of thesystem, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.

@inproceedings{grahl2007conceptuala, author = {Grahl, Miranda and Hotho, Andreas and Stumme, Gerd}, title = {Conceptual Clustering of Social Bookmarking Sites}, booktitle = {7th International Conference on Knowledge Management (I-KNOW '07)}, publisher = {Know-Center}, address = {Graz, Austria}, year = {2007}, pages = {356-364}, url = {http://www.tagora-project.eu/wp-content/2007/06/grahl_iknow07.pdf}, keywords = {2007, folksonomies, folksonomy, itegpub, myown, sites, social, tagging, tagorapub}, abstract = {Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of thesystem, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.} }

%0 = inproceedings %A = Grahl, Miranda and Hotho, Andreas and Stumme, Gerd %B = 7th International Conference on Knowledge Management (I-KNOW '07) %C = Graz, Austria %D = 2007 %I = Know-Center %T = Conceptual Clustering of Social Bookmarking Sites %U = http://www.tagora-project.eu/wp-content/2007/06/grahl_iknow07.pdf

Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of thesystem, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.

@inproceedings{grahl2007conceptuala, author = {Grahl, Miranda and Hotho, Andreas and Stumme, Gerd}, title = {Conceptual Clustering of Social Bookmarking Sites}, booktitle = {7th International Conference on Knowledge Management (I-KNOW '07)}, publisher = {Know-Center}, address = {Graz, Austria}, year = {2007}, pages = {356-364}, url = {http://www.tagora-project.eu/wp-content/2007/06/grahl_iknow07.pdf}, keywords = {sites, 2007, tagging, social, folksonomy, tagorapub, folksonomies, itegpub}, abstract = {Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of thesystem, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.} }

%0 = inproceedings %A = Grahl, Miranda and Hotho, Andreas and Stumme, Gerd %B = 7th International Conference on Knowledge Management (I-KNOW '07) %C = Graz, Austria %D = 2007 %I = Know-Center %T = Conceptual Clustering of Social Bookmarking Sites %U = http://www.tagora-project.eu/wp-content/2007/06/grahl_iknow07.pdf

@inproceedings{grahl07conceptualKdml, author = {Grahl, Miranda and Hotho, Andreas and Stumme, Gerd}, title = {Conceptual Clustering of Social Bookmark Sites}, editor = {Hinneburg, Alexander}, booktitle = {Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007)}, publisher = {Martin-Luther-Universität Halle-Wittenberg}, year = {2007}, pages = {50-54}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf}, isbn = {978-3-86010-907-6}, keywords = {2007, Social, bookmark, bookmarking, clustering, collaborative, conceptual, folksonomies, folksonomy, itegpub, myown, social, tagging, tagorapub} }

%0 = inproceedings %A = Grahl, Miranda and Hotho, Andreas and Stumme, Gerd %B = Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007) %D = 2007 %I = Martin-Luther-Universität Halle-Wittenberg %T = Conceptual Clustering of Social Bookmark Sites %U = http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf

Social tagging is one of the most important ways to organize and index online resources. Recommendation in social tagging systems, e.g. tag recommendation, item recommendation and user recommendation, is used to improve the quality of tags and to ease the tagging or searching process. Existing works usually provide recommendations by analyzing relation information in social tagging systems, suffering a lot from the over sparse problem. These approaches ignore information contained in the content of resources, which we believe should be considered to improve recommendation quality and to deal with the over sparse problem. In this paper we propose a recommendation approach for social tagging systems that combines content and relation analysis in a single model. By modeling the generating process of social tagging systems in a latent Dirichlet allocation approach, we build a fully generative model for social tagging, leverage it to estimate the relation between users, tags and resources and achieve tag, item and user recommendation tasks. The model is evaluated using a CiteULike data snapshot, and results show improvements in metrics for various recommendation tasks.

@article{Zhang20125759, author = {Zhang, Yin and Zhang, Bin and Gao, Kening and Guo, Pengwei and Sun, Daming}, title = {Combining content and relation analysis for recommendation in social tagging systems}, journal = {Physica A: Statistical Mechanics and its Applications}, year = {2012}, volume = {391}, number = {22}, pages = {5759 - 5768}, url = {http://www.sciencedirect.com/science/article/pii/S0378437112003846}, doi = {10.1016/j.physa.2012.05.013}, issn = {0378-4371}, keywords = {folkrank, folksonomy, lda, model, ranking, toread}, abstract = {Social tagging is one of the most important ways to organize and index online resources. Recommendation in social tagging systems, e.g. tag recommendation, item recommendation and user recommendation, is used to improve the quality of tags and to ease the tagging or searching process. Existing works usually provide recommendations by analyzing relation information in social tagging systems, suffering a lot from the over sparse problem. These approaches ignore information contained in the content of resources, which we believe should be considered to improve recommendation quality and to deal with the over sparse problem. In this paper we propose a recommendation approach for social tagging systems that combines content and relation analysis in a single model. By modeling the generating process of social tagging systems in a latent Dirichlet allocation approach, we build a fully generative model for social tagging, leverage it to estimate the relation between users, tags and resources and achieve tag, item and user recommendation tasks. The model is evaluated using a CiteULike data snapshot, and results show improvements in metrics for various recommendation tasks.} }

%0 = article %A = Zhang, Yin and Zhang, Bin and Gao, Kening and Guo, Pengwei and Sun, Daming %D = 2012 %T = Combining content and relation analysis for recommendation in social tagging systems %U = http://www.sciencedirect.com/science/article/pii/S0378437112003846

Social networks and collaborative tagging systems are rapidly gaining popularity as a primary means for storing and sharing data among friends, family, colleagues, or perfect strangers as long as they have common interests. del.icio.us is a social network where people store and share their personal bookmarks. Most importantly, users tag their bookmarks for ease of information dissemination and later look up. However, it is the friendship links, that make delicious a social network. They exist independently of the set of bookmarks that belong to the users and have no relation to the tags typically assigned to the bookmarks. To study the interaction among users, the strength of the existing links and their hidden meaning, we introduce implicit links in the network. These links connect only highly "similar" users. Here, similarity can reflect different aspects of the user’s profile that makes her similar to any other user, such as number of shared bookmarks, or similarity of their tags clouds. We investigate the question whether friends have common interests, we gain additional insights on the strategies that users use to assign tags to their bookmarks, and we demonstrate that the graphs formed by implicit links have unique properties differing from binomial random graphs or random graphs with an expected power-law degree distribution.

@inproceedings{angelova2008characterizing, author = {Angelova, Ralitsa and Lipczak, Marek and Milios, Evangelos and Prałat, Paweł}, title = {Characterizing a social bookmarking and tagging network}, booktitle = {Proceedings of the Mining Social Data Workshop (MSoDa)}, year = {2008}, pages = {21--25}, url = {http://www.math.ryerson.ca/~pralat/papers/2008_delicious.pdf}, keywords = {analysis, bookmarking, collaborative, folksonomy, network, tagging}, abstract = {Social networks and collaborative tagging systems are rapidly gaining popularity as a primary means for storing and sharing data among friends, family, colleagues, or perfect strangers as long as they have common interests. del.icio.us is a social network where people store and share their personal bookmarks. Most importantly, users tag their bookmarks for ease of information dissemination and later look up. However, it is the friendship links, that make delicious a social network. They exist independently of the set of bookmarks that belong to the users and have no relation to the tags typically assigned to the bookmarks. To study the interaction among users, the strength of the existing links and their hidden meaning, we introduce implicit links in the network. These links connect only highly "similar" users. Here, similarity can reflect different aspects of the user’s profile that makes her similar to any other user, such as number of shared bookmarks, or similarity of their tags clouds. We investigate the question whether friends have common interests, we gain additional insights on the strategies that users use to assign tags to their bookmarks, and we demonstrate that the graphs formed by implicit links have unique properties differing from binomial random graphs or random graphs with an expected power-law degree distribution. } }

%0 = inproceedings %A = Angelova, Ralitsa and Lipczak, Marek and Milios, Evangelos and Prałat, Paweł %B = Proceedings of the Mining Social Data Workshop (MSoDa) %D = 2008 %T = Characterizing a social bookmarking and tagging network %U = http://www.math.ryerson.ca/~pralat/papers/2008_delicious.pdf

Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.

@incollection{jaeschke2012challenges, author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, title = {Challenges in Tag Recommendations for Collaborative Tagging Systems}, editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.}, booktitle = {Recommender Systems for the Social Web}, series = {Intelligent Systems Reference Library}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2012}, volume = {32}, pages = {65--87}, url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3}, doi = {10.1007/978-3-642-25694-3_3}, isbn = {978-3-642-25694-3}, keywords = {2012, bookmarking, challenge, collaborative, dc09, discovery, folksonomy, myown, recommender, rsdc08, social, tagging}, abstract = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.} }

%0 = incollection %A = Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd %B = Recommender Systems for the Social Web %C = Berlin/Heidelberg %D = 2012 %I = Springer %T = Challenges in Tag Recommendations for Collaborative Tagging Systems %U = http://dx.doi.org/10.1007/978-3-642-25694-3_3

Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.

@incollection{jaeschke2012challenges, author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, title = {Challenges in Tag Recommendations for Collaborative Tagging Systems}, editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.}, booktitle = {Recommender Systems for the Social Web}, series = {Intelligent Systems Reference Library}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2012}, volume = {32}, pages = {65--87}, url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3}, doi = {10.1007/978-3-642-25694-3_3}, isbn = {978-3-642-25694-3}, keywords = {2012, bookmarking, challenge, collaborative, dc09, discovery, folksonomy, info20, itegpub, l3s, myown, recommender, rsdc08, social, tagging}, abstract = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.} }

%0 = incollection %A = Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd %B = Recommender Systems for the Social Web %C = Berlin/Heidelberg %D = 2012 %I = Springer %T = Challenges in Tag Recommendations for Collaborative Tagging Systems %U = http://dx.doi.org/10.1007/978-3-642-25694-3_3

Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.

@incollection{jaeschke2012challenges, author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, title = {Challenges in Tag Recommendations for Collaborative Tagging Systems}, editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.}, booktitle = {Recommender Systems for the Social Web}, series = {Intelligent Systems Reference Library}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2012}, volume = {32}, pages = {65--87}, url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3}, doi = {10.1007/978-3-642-25694-3_3}, isbn = {978-3-642-25694-3}, keywords = {2012, bookmarking, challenge, collaborative, dc09, discovery, folksonomy, myown, recommender, rsdc08, social, tagging}, abstract = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.} }

%0 = incollection %A = Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd %B = Recommender Systems for the Social Web %C = Berlin/Heidelberg %D = 2012 %I = Springer %T = Challenges in Tag Recommendations for Collaborative Tagging Systems %U = http://dx.doi.org/10.1007/978-3-642-25694-3_3

@inproceedings{yanbe2007can, author = {Yanbe, Yusuke and Jatowt, Adam and Nakamura, Satoshi and Tanaka, Katsumi}, title = {Can social bookmarking enhance search in the web?}, booktitle = {JCDL '07: Proceedings of the 2007 conference on Digital libraries}, publisher = {ACM Press}, address = {New York, NY, USA}, year = {2007}, pages = {107--116}, url = {http://portal.acm.org/citation.cfm?id=1255175.1255198}, doi = {http://doi.acm.org/10.1145/1255175.1255198}, isbn = {978-1-59593-644-8}, keywords = {search, logsonomy, social_bookmarking, folksonomy} }

%0 = inproceedings %A = Yanbe, Yusuke and Jatowt, Adam and Nakamura, Satoshi and Tanaka, Katsumi %B = JCDL '07: Proceedings of the 2007 conference on Digital libraries %C = New York, NY, USA %D = 2007 %I = ACM Press %T = Can social bookmarking enhance search in the web? %U = http://portal.acm.org/citation.cfm?id=1255175.1255198

Social bookmark tools are rapidly emerging on the Web. In suchsystems users are setting up lightweight conceptual structurescalled folksonomies. The reason for their immediate success is thefact that no specific skills are needed for participating. In thispaper we specify a formal model for folksonomies and briefly describe our own system BibSonomy, which allows for sharing both bookmarksand publication references in a kind of personal library.

@inproceedings{hotho2006bibsonomy, author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, title = {BibSonomy: A Social Bookmark and Publication Sharing System}, editor = {de Moor, Aldo and Polovina, Simon and Delugach, Harry}, booktitle = {Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures}, publisher = {Aalborg Universitetsforlag}, address = {Aalborg}, year = {2006}, pages = {87-102}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006bibsonomy.pdf}, isbn = {87-7307-769-0}, keywords = {2006, bookmarking, social, nepomuk, bibsonomy, OntologyHandbook, FCA, folksonomy, tagorapub, iccs, l3s}, abstract = {Social bookmark tools are rapidly emerging on the Web. In suchsystems users are setting up lightweight conceptual structurescalled folksonomies. The reason for their immediate success is thefact that no specific skills are needed for participating. In thispaper we specify a formal model for folksonomies and briefly describe our own system BibSonomy, which allows for sharing both bookmarksand publication references in a kind of personal library.} }

%0 = inproceedings %A = Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd %B = Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures %C = Aalborg %D = 2006 %I = Aalborg Universitetsforlag %T = BibSonomy: A Social Bookmark and Publication Sharing System %U = http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006bibsonomy.pdf

@article{peterson2006beneath, author = {Peterson, Elaine}, title = {Beneath the Metadata: Some Philosophical Problems with Folksonomy }, journal = {D-Lib Magazine}, year = {2006}, volume = {12}, number = {11}, url = {http://www.dlib.org/dlib/november06/peterson/11peterson.html}, doi = {10.1045/november2006-peterson}, issn = {1082-9873}, keywords = {folksonomy, metadata, philosophy} }

%0 = article %A = Peterson, Elaine %D = 2006 %T = Beneath the Metadata: Some Philosophical Problems with Folksonomy %U = http://www.dlib.org/dlib/november06/peterson/11peterson.html

@article{november06peterson, author = {Peterson, Elaine}, title = {Beneath the Metadata: Some Philosophical Problems with Folksonomy }, journal = {D-Lib Magazine}, year = {2006}, volume = {12}, number = {11}, url = {http://www.dlib.org/dlib/november06/peterson/11peterson.html}, doi = {10.1045/november2006-peterson}, issn = {1082-9873}, keywords = {folksonomies, folksonomy, relativism, tagging} }

%0 = article %A = Peterson, Elaine %D = 2006 %T = Beneath the Metadata: Some Philosophical Problems with Folksonomy %U = http://www.dlib.org/dlib/november06/peterson/11peterson.html

We propose an approach that unifies browsing by tags and visual features for intuitive exploration of image databases. Incontrast to traditional image retrieval approaches, we utilise tags provided by users on collaborative tagging sites, complementedby simple image analysis and classification. This allows us to find new relations between data elements. We introduce theconcept of a navigation map, that describes links between users, tags, and data elements for the example of the collaborativetagging site Flickr. We show that introducing similarity search based on image features yields additional links on this map.These theoretical considerations are supported by examples provided by our system, using data and tags from real Flickr users.

@inproceedings{aurnhammer2006augmenting, author = {Aurnhammer, Melanie and Hanappe, Peter and Steels, Luc}, title = {Augmenting Navigation for Collaborative Tagging with Emergent Semantics}, journal = {The Semantic Web - ISWC 2006}, year = {2006}, volume = {4273}, pages = {58--71}, url = {http://dx.doi.org/10.1007/11926078_5}, keywords = {folksonomy, semantics, ol_web2.0, emergentsemantics_evidence}, abstract = {We propose an approach that unifies browsing by tags and visual features for intuitive exploration of image databases. Incontrast to traditional image retrieval approaches, we utilise tags provided by users on collaborative tagging sites, complementedby simple image analysis and classification. This allows us to find new relations between data elements. We introduce theconcept of a navigation map, that describes links between users, tags, and data elements for the example of the collaborativetagging site Flickr. We show that introducing similarity search based on image features yields additional links on this map.These theoretical considerations are supported by examples provided by our system, using data and tags from real Flickr users.} }

%0 = inproceedings %A = Aurnhammer, Melanie and Hanappe, Peter and Steels, Luc %D = 2006 %T = Augmenting Navigation for Collaborative Tagging with Emergent Semantics %U = http://dx.doi.org/10.1007/11926078_5

BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.

@inproceedings{jaeschke2007analysis, author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Stumme, Gerd}, title = {Analysis of the Publication Sharing Behaviour in BibSonomy}, editor = {Priss, U. and Polovina, S. and Hill, R.}, booktitle = {Proceedings of the 15th International Conference on Conceptual Structures (ICCS 2007)}, series = {Lecture Notes in Artificial Intelligence}, publisher = {Springer-Verlag}, address = {Berlin, Heidelberg}, year = {2007}, volume = {4604}, pages = {283--295}, isbn = {3-540-73680-8}, keywords = {2007, BibSonomy, bibsonomy, bookmarking, fca, folksonomy, iccs, itegpub, l3s, myown, publication, sharing, social, trias}, abstract = {BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.} }

%0 = inproceedings %A = Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Stumme, Gerd %B = Proceedings of the 15th International Conference on Conceptual Structures (ICCS 2007) %C = Berlin, Heidelberg %D = 2007 %I = Springer-Verlag %T = Analysis of the Publication Sharing Behaviour in BibSonomy

In collaborative tagging systems, it is generally assumed that users assign tags to facilitate retrieval of content at a later time. There is, however, little behavioral evidence that tags actually serve this purpose. Using a large-scale dataset from the social music website Last.fm, we explore how patterns of music tagging and subsequent listening interact to determine if there exist measurable signals of tags functioning as retrieval aids. Specifically, we describe our methods for testing if the assignment of a tag tends to lead to an increase in listening behavior. Results suggest that tagging, on average, leads to only very small increases in listening rates, and overall the data do

@incollection{lorince2015analysis, author = {Lorince, Jared and Joseph, Kenneth and Todd, PeterM.}, title = {Analysis of Music Tagging and Listening Patterns: Do Tags Really Function as Retrieval Aids?}, editor = {Agarwal, Nitin and Xu, Kevin and Osgood, Nathaniel}, booktitle = {Social Computing, Behavioral-Cultural Modeling, and Prediction}, series = {Lecture Notes in Computer Science}, publisher = {Springer International Publishing}, year = {2015}, volume = {9021}, pages = {141-152}, url = {http://dx.doi.org/10.1007/978-3-319-16268-3_15}, doi = {10.1007/978-3-319-16268-3_15}, isbn = {978-3-319-16267-6}, keywords = {folksonomy, last.fm, retrieval, tagging, usage}, abstract = {In collaborative tagging systems, it is generally assumed that users assign tags to facilitate retrieval of content at a later time. There is, however, little behavioral evidence that tags actually serve this purpose. Using a large-scale dataset from the social music website Last.fm, we explore how patterns of music tagging and subsequent listening interact to determine if there exist measurable signals of tags functioning as retrieval aids. Specifically, we describe our methods for testing if the assignment of a tag tends to lead to an increase in listening behavior. Results suggest that tagging, on average, leads to only very small increases in listening rates, and overall the data do } }

%0 = incollection %A = Lorince, Jared and Joseph, Kenneth and Todd, PeterM. %B = Social Computing, Behavioral-Cultural Modeling, and Prediction %D = 2015 %I = Springer International Publishing %T = Analysis of Music Tagging and Listening Patterns: Do Tags Really Function as Retrieval Aids? %U = http://dx.doi.org/10.1007/978-3-319-16268-3_15

This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotationservices have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largelylowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might becomea key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations,for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervisedmodel to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.usas example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We furtherapply our model on another data set from Flickr to testify our model’s applicability on different environments. The experimentalresults demonstrate our model’s efficiency.

@inproceedings{zhou2008unsupervised, author = {Zhou, Mianwei and Bao, Shenghua and Wu, Xian and Yu, Yong}, title = {An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations}, journal = {The Semantic Web}, year = {2008}, pages = {680--693}, url = {http://dx.doi.org/10.1007/978-3-540-76298-0_49}, keywords = {folksonomy, learning, semantics, ol_web2.0, methods_concepthierarchy, data_tagging}, abstract = {This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotationservices have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largelylowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might becomea key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations,for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervisedmodel to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.usas example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We furtherapply our model on another data set from Flickr to testify our model’s applicability on different environments. The experimentalresults demonstrate our model’s efficiency.} }

%0 = inproceedings %A = Zhou, Mianwei and Bao, Shenghua and Wu, Xian and Yu, Yong %D = 2008 %T = An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations %U = http://dx.doi.org/10.1007/978-3-540-76298-0_49

@article{gasevic2011approach, author = {Gasevic, Dragan and Zouaq, Amal and Torniai, Carlo and Jovanovic, Jelena and Hatala, Marek}, title = {An Approach to Folksonomy-based Ontology Maintenance for Learning Environments}, journal = {IEEE Transactions on Learning Technologies}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, year = {2011}, volume = {99}, number = {1}, url = {http://www.computer.org/portal/web/csdl/doi/10.1109/TLT.2011.21}, doi = {10.1109/TLT.2011.21}, issn = {1939-1382}, keywords = {folksonomy, maintenance, ontology, tags} }

%0 = article %A = Gasevic, Dragan and Zouaq, Amal and Torniai, Carlo and Jovanovic, Jelena and Hatala, Marek %C = Los Alamitos, CA, USA %D = 2011 %I = IEEE Computer Society %T = An Approach to Folksonomy-based Ontology Maintenance for Learning Environments %U = http://www.computer.org/portal/web/csdl/doi/10.1109/TLT.2011.21

Since the rise of collaborative tagging systems on the web, the tag recommendation task - suggesting suitable tags to users of such systems while they add resources to their collection - has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.

@inproceedings{doerfel2013analysis, author = {Doerfel, Stephan and Jäschke, Robert}, title = {An analysis of tag-recommender evaluation procedures}, booktitle = {Proceedings of the 7th ACM conference on Recommender systems}, series = {RecSys '13}, publisher = {ACM}, address = {New York, NY, USA}, year = {2013}, pages = {343--346}, url = {https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf}, doi = {10.1145/2507157.2507222}, isbn = {978-1-4503-2409-0}, keywords = {2013, bibsonomy, bookmarking, collaborative, core, evaluation, folkrank, folksonomy, graph, iteg, itegpub, l3s, recommender, social, tagging}, abstract = {Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.} }

%0 = inproceedings %A = Doerfel, Stephan and Jäschke, Robert %B = Proceedings of the 7th ACM conference on Recommender systems %C = New York, NY, USA %D = 2013 %I = ACM %T = An analysis of tag-recommender evaluation procedures %U = https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf

Learning structured representations has emerged as an important problem in many domains, including document and Web data mining, bioinformatics, and image analysis. One approach to learning complex structures is to integrate many smaller, incomplete and noisy structure fragments. In this work, we present an unsupervised probabilistic approach that extends affinity propagation to combine the small ontological fragments into a collection of integrated, consistent, and larger folksonomies. This is a challenging task because the method must aggregate similar structures while avoiding structural inconsistencies and handling noise. We validate the approach on a real-world social media dataset, comprised of shallow personal hierarchies specified by many individual users, collected from the photosharing website Flickr. Our empirical results show that our proposed approach is able to construct deeper and denser structures, compared to an approach using only the standard affinity propagation algorithm. Additionally, the approach yields better overall integration quality than a state-of-the-art approach based on incremental relational clustering.

@inproceedings{plangprasopchok2010probabilistic, author = {Plangprasopchok, Anon and Lerman, Kristina and Getoor, Lise}, title = {A Probabilistic Approach for Learning Folksonomies from Structured Data}, booktitle = {Proceedings of the 4th ACM Web Search and Data Mining Conference}, year = {2010}, note = {cite arxiv:1011.3557Comment: In Proceedings of the 4th ACM Web Search and Data Mining Conference (WSDM)}, url = {http://arxiv.org/abs/1011.3557}, keywords = {affinity_propagation, deletethistag, folksonomy, learning, ontology}, abstract = {Learning structured representations has emerged as an important problem in many domains, including document and Web data mining, bioinformatics, and image analysis. One approach to learning complex structures is to integrate many smaller, incomplete and noisy structure fragments. In this work, we present an unsupervised probabilistic approach that extends affinity propagation to combine the small ontological fragments into a collection of integrated, consistent, and larger folksonomies. This is a challenging task because the method must aggregate similar structures while avoiding structural inconsistencies and handling noise. We validate the approach on a real-world social media dataset, comprised of shallow personal hierarchies specified by many individual users, collected from the photosharing website Flickr. Our empirical results show that our proposed approach is able to construct deeper and denser structures, compared to an approach using only the standard affinity propagation algorithm. Additionally, the approach yields better overall integration quality than a state-of-the-art approach based on incremental relational clustering. } }

%0 = inproceedings %A = Plangprasopchok, Anon and Lerman, Kristina and Getoor, Lise %B = Proceedings of the 4th ACM Web Search and Data Mining Conference %D = 2010 %T = A Probabilistic Approach for Learning Folksonomies from Structured Data %U = http://arxiv.org/abs/1011.3557

Researchers, as well as ordinary users who seek information in diverse academic fields, turn to the web to search for publications of interest. Even though scholarly publication recommenders have been developed to facilitate the task of discovering literature pertinent to their users, they (i) are not personalized enough to meet users' expectations, since they provide the same suggestions to users sharing similar profiles/preferences, (ii) generate recommendations pertaining to each user's general interests as opposed to the specific need of the user, and (iii) fail to take full advantages of valuable user-generated data at social websites that can enhance their performance. To address these problems, we propose PubRec, a recommender that suggests closely-related references to a particular publication P tailored to a specific user U, which minimizes the time and efforts imposed on U in browsing through general recommended publications. Empirical studies conducted using data extracted from CiteULike (i) verify the efficiency of the recommendation and ranking strategies adopted by PubRec and (ii) show that PubRec significantly outperforms other baseline recommenders.

@inproceedings{pera2011personalized, author = {Pera, Maria Soledad and Ng, Yiu-Kai}, title = {A personalized recommendation system on scholarly publications}, booktitle = {Proceedings of the 20th ACM international conference on Information and knowledge management}, publisher = {ACM}, address = {New York, NY, USA}, year = {2011}, pages = {2133--2136}, url = {http://doi.acm.org/10.1145/2063576.2063908}, doi = {10.1145/2063576.2063908}, isbn = {978-1-4503-0717-8}, keywords = {folksonomy, item, recommender, social, tagging}, abstract = {Researchers, as well as ordinary users who seek information in diverse academic fields, turn to the web to search for publications of interest. Even though scholarly publication recommenders have been developed to facilitate the task of discovering literature pertinent to their users, they (i) are not personalized enough to meet users' expectations, since they provide the same suggestions to users sharing similar profiles/preferences, (ii) generate recommendations pertaining to each user's general interests as opposed to the specific need of the user, and (iii) fail to take full advantages of valuable user-generated data at social websites that can enhance their performance. To address these problems, we propose PubRec, a recommender that suggests closely-related references to a particular publication P tailored to a specific user U, which minimizes the time and efforts imposed on U in browsing through general recommended publications. Empirical studies conducted using data extracted from CiteULike (i) verify the efficiency of the recommendation and ranking strategies adopted by PubRec and (ii) show that PubRec significantly outperforms other baseline recommenders.} }

%0 = inproceedings %A = Pera, Maria Soledad and Ng, Yiu-Kai %B = Proceedings of the 20th ACM international conference on Information and knowledge management %C = New York, NY, USA %D = 2011 %I = ACM %T = A personalized recommendation system on scholarly publications %U = http://doi.acm.org/10.1145/2063576.2063908

In this paper we consider the problem of item recommendation in collaborative tagging communities, so called folksonomies, where users annotate interesting items with tags. Rather than following a collaborative filtering or annotation-based approach to recommendation, we extend the probabilistic latent semantic analysis (PLSA) approach and present a unified recommendation model which evolves from item user and item tag co-occurrences in parallel. The inclusion of tags reduces known collaborative filtering problems related to overfitting and allows for higher quality recommendations. Experimental results on a large snapshot of the <i>delicious</i> bookmarking service show the scalability of our approach and an improved recommendation quality compared to two-mode collaborative or annotation based methods.

@inproceedings{wetzker2009hybrid, author = {Wetzker, Robert and Umbrath, Winfried and Said, Alan}, title = {A hybrid approach to item recommendation in folksonomies}, booktitle = {Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval}, series = {ESAIR '09}, publisher = {ACM}, address = {New York, NY, USA}, year = {2009}, pages = {25--29}, url = {http://doi.acm.org/10.1145/1506250.1506255}, doi = {10.1145/1506250.1506255}, isbn = {978-1-60558-430-0}, keywords = {folksonomy, itemRecommendation, plsa}, abstract = {In this paper we consider the problem of item recommendation in collaborative tagging communities, so called folksonomies, where users annotate interesting items with tags. Rather than following a collaborative filtering or annotation-based approach to recommendation, we extend the probabilistic latent semantic analysis (PLSA) approach and present a unified recommendation model which evolves from item user and item tag co-occurrences in parallel. The inclusion of tags reduces known collaborative filtering problems related to overfitting and allows for higher quality recommendations. Experimental results on a large snapshot of the <i>delicious</i> bookmarking service show the scalability of our approach and an improved recommendation quality compared to two-mode collaborative or annotation based methods.} }

%0 = inproceedings %A = Wetzker, Robert and Umbrath, Winfried and Said, Alan %B = Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval %C = New York, NY, USA %D = 2009 %I = ACM %T = A hybrid approach to item recommendation in folksonomies %U = http://doi.acm.org/10.1145/1506250.1506255

Folksonomies are becoming increasingly popular. They contain large amounts of data which can be mined and utilized for many tasks like visualization, browsing, information retrieval etc. An inherent problem of folksonomies is the lack of structure. In this paper we present an unsupervised approach for generating such structure based on a combination of association rule mining and the underlying tagged material. Using the underlying tagged material we generate a semantic representation of each tag. The semantic representation of the tags is an integral component of the structure generated. The experiment presented in this paper shows promising results with tag structures that correspond well with human judgment.

@incollection{solskinnsbakk2010hybrid, author = {Solskinnsbakk, Geir and Gulla, Jon}, title = {A Hybrid Approach to Constructing Tag Hierarchies}, editor = {Meersman, Robert and Dillon, Tharam and Herrero, Pilar}, booktitle = {On the Move to Meaningful Internet Systems, OTM 2010}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin / Heidelberg}, year = {2010}, volume = {6427}, pages = {975-982}, url = {http://dx.doi.org/10.1007/978-3-642-16949-6_22}, doi = {10.1007/978-3-642-16949-6_22}, isbn = {978-3-642-16948-9}, keywords = {folksonomy, learning, ontology}, abstract = {Folksonomies are becoming increasingly popular. They contain large amounts of data which can be mined and utilized for many tasks like visualization, browsing, information retrieval etc. An inherent problem of folksonomies is the lack of structure. In this paper we present an unsupervised approach for generating such structure based on a combination of association rule mining and the underlying tagged material. Using the underlying tagged material we generate a semantic representation of each tag. The semantic representation of the tags is an integral component of the structure generated. The experiment presented in this paper shows promising results with tag structures that correspond well with human judgment.} }

%0 = incollection %A = Solskinnsbakk, Geir and Gulla, Jon %B = On the Move to Meaningful Internet Systems, OTM 2010 %C = Berlin / Heidelberg %D = 2010 %I = Springer %T = A Hybrid Approach to Constructing Tag Hierarchies %U = http://dx.doi.org/10.1007/978-3-642-16949-6_22

Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system's data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users.</p> <p>In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings.</p> <p>Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.

@inproceedings{krause2008comparison, author = {Krause, Beate and Hotho, Andreas and Stumme, Gerd}, title = {A comparison of social bookmarking with traditional search}, booktitle = {Proceedings of the IR research, 30th European conference on Advances in information retrieval}, series = {ECIR'08}, publisher = {Springer-Verlag}, address = {Berlin, Heidelberg}, year = {2008}, pages = {101--113}, url = {http://dl.acm.org/citation.cfm?id=1793274.1793290}, isbn = {3-540-78645-7, 978-3-540-78645-0}, keywords = {bookmarking, comparison, folksonomy, ranking, search, social}, abstract = {Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system's data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users.</p> <p>In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings.</p> <p>Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.} }

%0 = inproceedings %A = Krause, Beate and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the IR research, 30th European conference on Advances in information retrieval %C = Berlin, Heidelberg %D = 2008 %I = Springer-Verlag %T = A comparison of social bookmarking with traditional search %U = http://dl.acm.org/citation.cfm?id=1793274.1793290

Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.

@inproceedings{illig2009comparison, author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, title = {A Comparison of Content-Based Tag Recommendations in Folksonomy Systems}, editor = {Wolff, Karl Erich and Palchunov, Dmitry E. and Zagoruiko, Nikolay G. and Andelfinger, Urs}, booktitle = {Knowledge Processing and Data Analysis}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2011}, volume = {6581}, pages = {136--149}, url = {http://dx.doi.org/10.1007/978-3-642-22140-8_9}, doi = {10.1007/978-3-642-22140-8_9}, isbn = {978-3-642-22139-2}, keywords = {2011, content, folksonomy, myown, recommendations, recommender, tag}, abstract = {Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. } }

%0 = inproceedings %A = Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd %B = Knowledge Processing and Data Analysis %C = Berlin/Heidelberg %D = 2011 %I = Springer %T = A Comparison of Content-Based Tag Recommendations in Folksonomy Systems %U = http://dx.doi.org/10.1007/978-3-642-22140-8_9

Recommendation algorithms and multi-class classifiers can support
ers of social bookmarking systems in assigning tags to their
okmarks. Content based recommenders are the usual approach for
cing the cold start problem, i.e., when a bookmark is uploaded for
e first time and no information from other users can be exploited.
this paper, we evaluate several recommendation algorithms in a
ld-start scenario on a large real-world dataset.

@inproceedings{illig2009comparison, author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, title = {A Comparison of Content-Based Tag Recommendations in Folksonomy Systems}, editor = {Wolff, Karl Erich and Palchunov, Dmitry E. and Zagoruiko, Nikolay G. and Andelfinger, Urs}, booktitle = {Knowledge Processing and Data Analysis}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2011}, volume = {6581}, pages = {136--149}, url = {http://dx.doi.org/10.1007/978-3-642-22140-8_9}, doi = {10.1007/978-3-642-22140-8_9}, isbn = {978-3-642-22139-2}, keywords = {2011, content, folksonomy, info20, itegpub, l3s, myown, recommendations, recommender, tag, tagorapub}, abstract = {Recommendation algorithms and multi-class classifiers can support
ers of social bookmarking systems in assigning tags to their
okmarks. Content based recommenders are the usual approach for
cing the cold start problem, i.e., when a bookmark is uploaded for
e first time and no information from other users can be exploited.
this paper, we evaluate several recommendation algorithms in a
ld-start scenario on a large real-world dataset.
} }

%0 = inproceedings %A = Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd %B = Knowledge Processing and Data Analysis %C = Berlin/Heidelberg %D = 2011 %I = Springer %T = A Comparison of Content-Based Tag Recommendations in Folksonomy Systems %U = http://dx.doi.org/10.1007/978-3-642-22140-8_9

Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.

@inproceedings{illig2011comparison, author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, title = {A Comparison of Content-Based Tag Recommendations in Folksonomy Systems}, editor = {Wolff, Karl Erich and Palchunov, Dmitry E. and Zagoruiko, Nikolay G. and Andelfinger, Urs}, booktitle = {Knowledge Processing and Data Analysis}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, address = {Berlin/Heidelberg}, year = {2011}, volume = {6581}, pages = {136--149}, url = {http://dx.doi.org/10.1007/978-3-642-22140-8_9}, doi = {10.1007/978-3-642-22140-8_9}, isbn = {978-3-642-22139-2}, keywords = {2011, comparison, content, folksonomy, recommendations}, abstract = {Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset. } }

%0 = inproceedings %A = Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd %B = Knowledge Processing and Data Analysis %C = Berlin/Heidelberg %D = 2011 %I = Springer %T = A Comparison of Content-Based Tag Recommendations in Folksonomy Systems %U = http://dx.doi.org/10.1007/978-3-642-22140-8_9

@inproceedings{illig2011comparison, author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, title = {A Comparison of content-based Tag Recommendations in Folksonomy Systems}, booktitle = {Postproceedings of the International Conference on Knowledge Processing in Practice (KPP 2007)}, publisher = {Springer}, year = {2011}, keywords = {2011, content, folksonomy, itegpub, l3s, myown, recommendations, recommender, tag, tagorapub} }

%0 = inproceedings %A = Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd %B = Postproceedings of the International Conference on Knowledge Processing in Practice (KPP 2007) %D = 2011 %I = Springer %T = A Comparison of content-based Tag Recommendations in Folksonomy Systems

@inproceedings{illigtoappearcomparison, author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, title = {A Comparison of content-based Tag Recommendations in Folksonomy Systems}, booktitle = {Postproceedings of the International Conference on Knowledge Processing in Practice (KPP2007)}, publisher = {Springer}, year = {to appear}, keywords = {content, 2009, nopaper, tag, recommender, recommendations, folksonomy, tagorapub} }

%0 = inproceedings %A = Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd %B = Postproceedings of the International Conference on Knowledge Processing in Practice (KPP2007) %D = to appear %I = Springer %T = A Comparison of content-based Tag Recommendations in Folksonomy Systems