applications have become more and more popular. Their users record
data during their daily routine or specifically for certain events.
The application WideNoise Plus allows users to record sound samples
and to annotate them with perceptions and tags. The app is being
used to document and map the soundscape all over the world. The procedure
of recording, including the assignment of tags, has to be as easy-to-use
as possible. We therefore discuss the application of tag recommender
algorithms in this particular scenario. We show, that this task is
fundamentally different from the well-known tag recommendation problem
in folksonomies as users do no longer tag fix resources but rather
sensory data and impressions. The scenario requires efficient recommender
algorithms that are able to run on the mobile device, since Internet
connectivity cannot be assumed to be available. Therefore, we evaluate
the performance of several tag recommendation algorithms and discuss
their applicability in the mobile sensing use-case. ER - TY - CONF AU - Mueller, Juergen AU - Doerfel, Stephan AU - Becker, Martin AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Tag Recommendations for SensorFolkSonomies T2 - Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings PB - ACM C1 - PY - 2013/ CY - VL - IS - SP - EP - UR - DO - KW - 2013 KW - RecSys KW - everyaware KW - folksonomy KW - iteg KW - itegpub KW - l3s KW - myown KW - recommendation KW - rsweb KW - sensor KW - sitc KW - tag KW - widenoise L1 - SN - N1 - N1 - AB - With the rising popularity of smart mobile devices, sensor data-based
applications have become more and more popular. Their users record
data during their daily routine or specifically for certain events.
The application WideNoise Plus allows users to record sound samples
and to annotate them with perceptions and tags. The app is being
used to document and map the soundscape all over the world. The procedure
of recording, including the assignment of tags, has to be as easy-to-use
as possible. We therefore discuss the application of tag recommender
algorithms in this particular scenario. We show, that this task is
fundamentally different from the well-known tag recommendation problem
in folksonomies as users do no longer tag fix resources but rather
sensory data and impressions. The scenario requires efficient recommender
algorithms that are able to run on the mobile device, since Internet
connectivity cannot be assumed to be available. Therefore, we evaluate
the performance of several tag recommendation algorithms and discuss
their applicability in the mobile sensing use-case. ER - TY - CONF AU - Mueller, Juergen AU - Doerfel, Stephan AU - Becker, Martin AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Tag Recommendations for SensorFolkSonomies T2 - Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings PB - ACM C1 - PY - 2013/ CY - VL - IS - SP - EP - UR - DO - KW - 2013 KW - RecSys KW - everyaware KW - folksonomy KW - myown KW - recommendation KW - rsweb KW - sensor KW - tag KW - widenoise L1 - SN - N1 - N1 - AB - With the rising popularity of smart mobile devices, sensor data-based
applications have become more and more popular. Their users record
data during their daily routine or specifically for certain events.
The application WideNoise Plus allows users to record sound samples
and to annotate them with perceptions and tags. The app is being
used to document and map the soundscape all over the world. The procedure
of recording, including the assignment of tags, has to be as easy-to-use
as possible. We therefore discuss the application of tag recommender
algorithms in this particular scenario. We show, that this task is
fundamentally different from the well-known tag recommendation problem
in folksonomies as users do no longer tag fix resources but rather
sensory data and impressions. The scenario requires efficient recommender
algorithms that are able to run on the mobile device, since Internet
connectivity cannot be assumed to be available. Therefore, we evaluate
the performance of several tag recommendation algorithms and discuss
their applicability in the mobile sensing use-case. ER - TY - CONF AU - Rezel, R. AU - Liang, S. A2 - T1 - SWE-FE: Extending folksonomies to the Sensor Web T2 - 2010 International Symposium on Collaborative Technologies and Systems (CTS) PB - IEEE C1 - PY - 2010/05 CY - VL - IS - SP - 349 EP - 356 UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5478494 DO - 10.1109/CTS.2010.5478494 KW - collaborative KW - everyaware KW - folksonomy KW - sensor KW - tagging KW - taggingsurvey KW - toread L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Cattuto, Ciro AU - Benz, Dominik AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Semantic Grounding of Tag Relatedness in Social Bookmarking Systems T2 - The Semantic Web - ISWC 2008 PB - Springer Berlin / Heidelberg C1 - PY - 2008/ CY - VL - 5318 IS - SP - 615 EP - 631 UR - http://www.kde.cs.uni-kassel.de/pub/pdf/cattuto2008semantica.pdf DO - 10.1007/978-3-540-88564-1_39 KW - 2008 KW - folksonomy KW - grounding KW - iswc2008 KW - myown KW - semantic KW - sw KW - tag KW - tagging KW - taggingsurvey KW - webzu L1 - SN - 978-3-540-88563-4 N1 - SpringerLink - Buchkapitel N1 - AB - 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. ER - TY - CHAP AU - Cantador, Iván AU - Bellogín, Alejandro AU - Fernández-Tobías, Ignacio AU - López-Hernández, Sergio A2 - Huemer, Christian A2 - Setzer, Thomas A2 - Aalst, Wil A2 - Mylopoulos, John A2 - Rosemann, Michael A2 - Shaw, Michael J. A2 - Szyperski, Clemens T1 - Semantic Contextualisation of Social Tag-Based Profiles and Item Recommendations T2 - E-Commerce and Web Technologies PB - Springer C1 - Berlin/Heidelberg PY - 2011/ VL - 85 IS - SP - 101 EP - 113 UR - http://dx.doi.org/10.1007/978-3-642-23014-1_9 DO - 10.1007/978-3-642-23014-1_9 KW - folksonomy KW - item KW - recommender KW - semantics KW - tagging L1 - SN - 978-3-642-23014-1 N1 - N1 - AB - 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. ER - TY - CONF AU - Abbasi, Rabeeh AU - Staab, Steffen A2 - T1 - RichVSM: enRiched Vector Space Models for Folksonomies T2 - HyperText'09: Proceedings of 20th ACM conference on Hypertext and Hypermedia PB - C1 - PY - 2009/ CY - VL - IS - SP - EP - UR - DO - KW - folksonomy KW - richvsm KW - vector_space L1 - SN - N1 - N1 - AB - ER - TY - CHAP AU - Gemmell, Jonathan AU - Schimoler, Thomas AU - Mobasher, Bamshad AU - Burke, Robin A2 - Buccafurri, Francesco A2 - Semeraro, Giovanni T1 - Resource Recommendation in Collaborative Tagging Applications T2 - E-Commerce and Web Technologies PB - Springer C1 - Berlin/Heidelberg PY - 2010/ VL - 61 IS - SP - 1 EP - 12 UR - http://dx.doi.org/10.1007/978-3-642-15208-5_1 DO - 10.1007/978-3-642-15208-5_1 KW - collaborative KW - folksonomy KW - item KW - recommender KW - tagging L1 - SN - 978-3-642-15208-5 N1 - N1 - AB - 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. ER - TY - BOOK AU - Balby Marinho, L. AU - Hotho, A. AU - Jäschke, R. AU - Nanopoulos, A. AU - Rendle, S. AU - Schmidt-Thieme, L. AU - Stumme, G. AU - Symeonidis, P. A2 - T1 - Recommender Systems for Social Tagging Systems PB - Springer C1 - PY - 2012/02 VL - IS - SP - EP - UR - http://link.springer.com/book/10.1007/978-1-4614-1894-8 DO - 10.1007/978-1-4614-1894-8 KW - 2012 KW - bookmarking KW - collaborative KW - folksonomy KW - info20 KW - itegpub KW - l3s KW - myown KW - recommender KW - social KW - tagging KW - tagging KW - 2012 L1 - SN - 978-1-4614-1893-1 N1 - N1 - AB - 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. ER - TY - BOOK AU - Balby Marinho, L. AU - Hotho, A. AU - Jäschke, R. AU - Nanopoulos, A. AU - Rendle, S. AU - Schmidt-Thieme, L. AU - Stumme, G. AU - Symeonidis, P. A2 - T1 - Recommender Systems for Social Tagging Systems PB - Springer C1 - PY - 2012/02 VL - IS - SP - EP - UR - http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1 DO - KW - 2012 KW - bookmarking KW - collaborative KW - folksonomy KW - myown KW - recommender KW - social KW - tagging L1 - SN - 978-1-4614-1893-1 N1 - N1 - AB - 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. ER - TY - BOOK AU - Balby Marinho, L. AU - Hotho, A. AU - Jäschke, R. AU - Nanopoulos, A. AU - Rendle, S. AU - Schmidt-Thieme, L. AU - Stumme, G. AU - Symeonidis, P. A2 - T1 - Recommender Systems for Social Tagging Systems PB - Springer C1 - PY - 2012/02 VL - IS - SP - EP - UR - http://link.springer.com/book/10.1007/978-1-4614-1894-8 DO - 10.1007/978-1-4614-1894-8 KW - 2012 KW - bookmarking KW - collaborative KW - folksonomy KW - myown KW - recommender KW - social KW - tagging L1 - SN - 978-1-4614-1893-1 N1 - N1 - AB - 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. ER - TY - THES AU - Bogers, Toine T1 - Recommender Systems for Social Bookmarking PY - 2009/12 PB - Tilburg University SP - EP - UR - http://ilk.uvt.nl/~toine/phd-thesis/ DO - KW - bookmarking KW - dissertation KW - folksonomy KW - recommender KW - social KW - tagging KW - taggingsurvey L1 - N1 - N1 - AB - 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. ER - TY - THES AU - Bogers, Toine T1 - Recommender Systems for Social Bookmarking PY - 2009/12 PB - Tilburg University SP - EP - UR - http://ilk.uvt.nl/~toine/phd-thesis/ DO - KW - bookmarking KW - tagging KW - social KW - item KW - recommender KW - folksonomy KW - from:jaeschke KW - thesis L1 - N1 - N1 - AB - 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. ER - TY - JOUR AU - Benz, Dominik AU - Hotho, Andreas AU - Jäschke, Robert AU - Krause, Beate AU - Stumme, Gerd T1 - Query Logs as Folksonomies JO - Datenbank-Spektrum PY - 2010/06 VL - 10 IS - 1 SP - 15 EP - 24 UR - http://dx.doi.org/10.1007/s13222-010-0004-8 DO - KW - 2010 KW - folksonomy KW - itegpub KW - logsonomy KW - myown L1 - SN - N1 - N1 - AB - 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. ER - TY - JOUR AU - Benz, Dominik AU - Hotho, Andreas AU - Jäschke, Robert AU - Krause, Beate AU - Stumme, Gerd T1 - Query Logs as Folksonomies JO - Datenbank-Spektrum PY - 2010/06 VL - 10 IS - 1 SP - 15 EP - 24 UR - http://dx.doi.org/10.1007/s13222-010-0004-8 DO - KW - 2010 KW - folksonomy KW - itegpub KW - logsonomy KW - myown L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Helic, D. AU - Strohmaier, M. AU - Trattner, C. AU - Muhr, M. AU - Lerman, K. A2 - T1 - Pragmatic Evaluation of Folksonomies T2 - 20th International World Wide Web Conference (WWW2011), Hyderabad, India, March 28 - April 1, ACM PB - C1 - PY - 2011/ CY - VL - IS - SP - EP - UR - DO - KW - evaluation KW - folksonomy KW - pragmatic L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Kim, Heung-Nam AU - El Saddik, Abdulmotaleb A2 - T1 - Personalized PageRank vectors for tag recommendations: inside FolkRank T2 - Proceedings of the fifth ACM conference on Recommender systems PB - ACM C1 - New York, NY, USA PY - 2011/ CY - VL - IS - SP - 45 EP - 52 UR - http://doi.acm.org/10.1145/2043932.2043945 DO - 10.1145/2043932.2043945 KW - bookmarking KW - collaborative KW - folkrank KW - folksonomy KW - ranking KW - search KW - tagging KW - web KW - pagerank L1 - SN - 978-1-4503-0683-6 N1 - N1 - AB - 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. ER - TY - CONF AU - Clements, M. A2 - T1 - Personalization of Social Media T2 - Proceedings of BCS IRSG Symposium: Future Directions in Information Access 2007 PB - C1 - PY - 2007/08 CY - VL - IS - SP - EP - UR - DO - KW - collaborative KW - folksonomy KW - media KW - personalization KW - recommender KW - social KW - tagging L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Heckner, Markus AU - Heilemann, Michael AU - Wolff, Christian A2 - T1 - Personal Information Management vs. Resource Sharing: Towards a Model of Information Behaviour in Social Tagging Systems T2 - Int'l AAAI Conference on Weblogs and Social Media (ICWSM) PB - C1 - San Jose, CA, USA PY - 2009/05 CY - VL - IS - SP - EP - UR - DO - KW - bibsonomy KW - folksonomy KW - information KW - management KW - motivation KW - social KW - tagging KW - toread L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Rendle, Steffen AU - Schmidt-Thieme, Lars A2 - T1 - Pairwise interaction tensor factorization for personalized tag recommendation T2 - Proceedings of the third ACM international conference on Web search and data mining PB - ACM C1 - New York, NY, USA PY - 2010/ CY - VL - IS - SP - 81 EP - 90 UR - http://doi.acm.org/10.1145/1718487.1718498 DO - 10.1145/1718487.1718498 KW - collaborative KW - factorization KW - folksonomy KW - personalization KW - recommender KW - tag KW - tagging KW - tensor L1 - SN - 978-1-60558-889-6 N1 - N1 - AB - 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.
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. ER - TY - CONF AU - Konstas, Ioannis AU - Stathopoulos, Vassilios AU - Jose, Joemon M. A2 - T1 - On social networks and collaborative recommendation T2 - Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval PB - ACM C1 - New York, NY, USA PY - 2009/ CY - VL - IS - SP - 195 EP - 202 UR - http://doi.acm.org/10.1145/1571941.1571977 DO - 10.1145/1571941.1571977 KW - collaborative KW - folksonomy KW - random KW - recommender KW - tagging KW - walk L1 - SN - 978-1-60558-483-6 N1 - N1 - AB - 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. ER - TY - RPRT AU - Doerfel, Stephan AU - Zoller, Daniel AU - Singer, Philipp AU - Niebler, Thomas AU - Hotho, Andreas AU - Strohmaier, Markus A2 - T1 - Of course we share! Testing Assumptions about Social Tagging Systems PB - AD - PY - 2014/ VL - IS - SP - EP - UR - http://arxiv.org/abs/1401.0629 DO - KW - 2014 KW - analysis KW - assumptions KW - bibsonomy KW - data KW - folksonomy KW - log KW - myown KW - share KW - social KW - tagging KW - testing KW - weblog L1 - N1 - Of course we share! Testing Assumptions about Social Tagging Systems N1 - N1 - AB - Social tagging systems have established themselves as an important part in
today's web and have attracted the interest from our research community in a
variety of investigations. The overall vision of our community is that simply
through interactions with the system, i.e., through tagging and sharing of
resources, users would contribute to building useful semantic structures as
well as resource indexes using uncontrolled vocabulary not only due to the
easy-to-use mechanics. Henceforth, a variety of assumptions about social
tagging systems have emerged, yet testing them has been difficult due to the
absence of suitable data. In this work we thoroughly investigate three
available assumptions - e.g., is a tagging system really social? - by examining
live log data gathered from the real-world public social tagging system
BibSonomy. Our empirical results indicate that while some of these assumptions
hold to a certain extent, other assumptions need to be reflected and viewed in
a very critical light. Our observations have implications for the design of
future search and other algorithms to better reflect the actual user behavior. ER - TY - CONF AU - Schmitz, Christoph AU - Grahl, Miranda AU - Hotho, Andreas AU - Stumme, Gerd AU - Catutto, Ciro AU - Baldassarri, Andrea AU - Loreto, Vittorio AU - Servedio, Vito D. P. A2 - T1 - Network Properties of Folksonomies T2 - Proc. WWW2007 Workshop ``Tagging and Metadata for Social Information Organization'' PB - C1 - Banff PY - 2007/05 CY - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2007/schmitz07network.pdf DO - KW - 2007 KW - emergent KW - fca KW - folksonomy KW - folksononomies KW - itegpub KW - l3s KW - myown KW - semantics KW - smallworld KW - sna KW - socialnetwork L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Cattuto, Ciro AU - Schmitz, Christoph AU - Baldassarri, Andrea AU - Servedio, Vito D. P. AU - Loreto, Vittorio AU - Hotho, Andreas AU - Grahl, Miranda AU - Stumme, Gerd T1 - Network Properties of Folksonomies JO - AI Communications Journal, Special Issue on ``Network Analysis in Natural Sciences and Engineering'' PY - 2007/ VL - 20 IS - 4 SP - 245 EP - 262 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2007/cattuto2007network.pdf DO - KW - 2007 KW - emergent KW - fca KW - folksonomies KW - folksonomy KW - itegpub KW - l3s KW - myown KW - network KW - semantics KW - tagorapub L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Cattuto, Ciro AU - Schmitz, Christoph AU - Baldassarri, Andrea AU - Servedio, Vito D. P. AU - Loreto, Vittorio AU - Hotho, Andreas AU - Grahl, Miranda AU - Stumme, Gerd T1 - Network Properties of Folksonomies JO - AI Communications Journal, Special Issue on ``Network Analysis in Natural Sciences and Engineering'' PY - 2007/ VL - 20 IS - 4 SP - 245 EP - 262 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2007/cattuto2007network.pdf DO - KW - 2007 KW - semantics KW - emergent KW - folksonomy KW - tagorapub KW - folksonomies KW - l3s KW - network KW - itegpub KW - fca L1 - SN - N1 - See http://www.bibsonomy.org/bibtex/2e1a5234a896b1f422473b1fe5d91e26b/stumme for a shorter workshop version. N1 - AB - ER - TY - CONF AU - Schmitz, Christoph AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - Batagelj, V. A2 - Bock, H.-H. A2 - Ferligoj, A. A2 - v Ziberna, A. T1 - Mining Association Rules in Folksonomies T2 - Data Science and Classification: Proc. of the 10th IFCS Conf. PB - Springer C1 - Berlin, Heidelberg PY - 2006/ CY - VL - IS - SP - 261 EP - 270 UR - DO - KW - 2006 KW - FCA KW - OntologyHandbook KW - association KW - folksonomy KW - itegpub KW - myown KW - rule L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Schmitz, Christoph AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - Batagelj, V. A2 - Bock, H.-H. A2 - Ferligoj, A. A2 - �iberna, A. T1 - Mining Association Rules in Folksonomies T2 - Data Science and Classification. Proceedings of the 10th IFCS Conf. PB - Springer C1 - Heidelberg PY - 2006/07 CY - VL - IS - SP - 261 EP - 270 UR - DO - KW - analysis KW - closely_related KW - diploma_thesis KW - folksonomy KW - nepomuk KW - network KW - semantic KW - ol_web2.0 KW - methods_concepts KW - methods_concepthierarchy L1 - schmitz06-mining.pdf SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Christiaens, Stijn A2 - T1 - Metadata Mechanisms: From Ontology to Folksonomy ... and Back T2 - Lecture Notes in Computer Science: On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops PB - Springer C1 - PY - 2006/ CY - VL - IS - SP - EP - UR - http://www.springerlink.com/content/m370107220473394 DO - KW - diploma_thesis KW - faceted_classification KW - folksonomy KW - folksonomy_background KW - ontology KW - semantic_web KW - tagging KW - ol_web2.0 KW - background KW - widely_related L1 - christiaens06-metadata.pdf SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Laniado, David AU - Mika, Peter A2 - Patel-Schneider, Peter F. A2 - Pan, Yue A2 - Hitzler, Pascal A2 - Mika, Peter A2 - Zhang, Lei A2 - Pan, Jeff Z. A2 - Horrocks, Ian A2 - Glimm, Birte T1 - Making Sense of Twitter. T2 - International Semantic Web Conference (1) PB - Springer C1 - PY - 2010/ CY - VL - 6496 IS - SP - 470 EP - 485 UR - http://dblp.uni-trier.de/db/conf/semweb/iswc2010-1.html#LaniadoM10 DO - KW - analysis KW - folksonomy KW - tagging KW - toread KW - twitter L1 - SN - 978-3-642-17745-3 N1 - N1 - AB - ER - TY - CONF AU - Jäschke, Robert AU - Krause, Beate AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Logsonomy -- A Search Engine Folksonomy T2 - Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008) PB - AAAI Press C1 - PY - 2008/ CY - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf DO - KW - 2008 KW - engine KW - folksonomies KW - folksonomy KW - itegpub KW - logsonomies KW - logsonomy KW - myown KW - search KW - tagorapub L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Jäschke, Robert AU - Krause, Beate AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Logsonomy -- A Search Engine Folksonomy T2 - Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008) PB - AAAI Press C1 - PY - 2008/ CY - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/hotho/pub/2008/Krause2008logsonomy_short.pdf DO - KW - 2008 KW - search KW - engine KW - logsonomy KW - logsonomies KW - folksonomy KW - tagorapub KW - folksonomies KW - itegpub L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Krause, Beate AU - Jäschke, Robert AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Logsonomy - Social Information Retrieval with Logdata T2 - HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia PB - ACM C1 - New York, NY, USA PY - 2008/ CY - VL - IS - SP - 157 EP - 166 UR - 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 DO - http://doi.acm.org/10.1145/1379092.1379123 KW - folksonomy KW - information KW - logsonomy KW - retrieval KW - social L1 - SN - 978-1-59593-985-2 N1 - N1 - AB - Social bookmarking systems constitute an established
part of the Web 2.0. In such systems
users describe bookmarks by keywords
called tags. The structure behind these social
systems, called folksonomies, can be viewed
as a tripartite hypergraph of user, tag and resource
nodes. This underlying network shows
specific structural properties that explain its
growth and the possibility of serendipitous
exploration.
Today’s search engines represent the gateway
to retrieve information from the World Wide
Web. Short queries typically consisting of
two to three words describe a user’s information
need. In response to the displayed
results of the search engine, users click on
the links of the result page as they expect
the answer to be of relevance.
This clickdata can be represented as a folksonomy
in which queries are descriptions of
clicked URLs. The resulting network structure,
which we will term logsonomy is very
similar to the one of folksonomies. In order
to find out about its properties, we analyze
the topological characteristics of the tripartite
hypergraph of queries, users and bookmarks
on a large snapshot of del.icio.us and
on query logs of two large search engines.
All of the three datasets show small world
properties. The tagging behavior of users,
which is explained by preferential attachment
of the tags in social bookmark systems, is
reflected in the distribution of single query
words in search engines. We can conclude
that the clicking behaviour of search engine
users based on the displayed search results
and the tagging behaviour of social bookmarking
users is driven by similar dynamics. ER - TY - CONF AU - Krause, Beate AU - Jäschke, Robert AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Logsonomy - Social Information Retrieval with Logdata T2 - HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia PB - ACM C1 - New York, NY, USA PY - 2008/ CY - VL - IS - SP - 157 EP - 166 UR - 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 DO - http://doi.acm.org/10.1145/1379092.1379123 KW - 2.0 KW - 2008 KW - analysis KW - folksonomy KW - information KW - itegpub KW - logsonomy KW - myown KW - network KW - retrieval KW - search KW - social KW - tagorapub KW - web KW - web2.0 KW - web20 L1 - SN - 978-1-59593-985-2 N1 - N1 - AB - Social bookmarking systems constitute an established
part of the Web 2.0. In such systems
users describe bookmarks by keywords
called tags. The structure behind these social
systems, called folksonomies, can be viewed
as a tripartite hypergraph of user, tag and resource
nodes. This underlying network shows
specific structural properties that explain its
growth and the possibility of serendipitous
exploration.
Today’s search engines represent the gateway
to retrieve information from the World Wide
Web. Short queries typically consisting of
two to three words describe a user’s information
need. In response to the displayed
results of the search engine, users click on
the links of the result page as they expect
the answer to be of relevance.
This clickdata can be represented as a folksonomy
in which queries are descriptions of
clicked URLs. The resulting network structure,
which we will term logsonomy is very
similar to the one of folksonomies. In order
to find out about its properties, we analyze
the topological characteristics of the tripartite
hypergraph of queries, users and bookmarks
on a large snapshot of del.icio.us and
on query logs of two large search engines.
All of the three datasets show small world
properties. The tagging behavior of users,
which is explained by preferential attachment
of the tags in social bookmark systems, is
reflected in the distribution of single query
words in search engines. We can conclude
that the clicking behaviour of search engine
users based on the displayed search results
and the tagging behaviour of social bookmarking
users is driven by similar dynamics. ER - TY - CONF AU - Lipczak, Marek AU - Milios, Evangelos A2 - T1 - Learning in efficient tag recommendation T2 - Proceedings of the fourth ACM conference on Recommender systems PB - ACM C1 - New York, NY, USA PY - 2010/ CY - VL - IS - SP - 167 EP - 174 UR - http://doi.acm.org/10.1145/1864708.1864741 DO - 10.1145/1864708.1864741 KW - 2010 KW - collaborative KW - folksonomy KW - recommender KW - tagging L1 - SN - 978-1-60558-906-0 N1 - N1 - AB - 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. ER - TY - JOUR AU - Cimiano, Philipp AU - Hotho, Andreas AU - Staab, Steffen T1 - Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis JO - Journal on Artificial Intelligence Research PY - 2005/ VL - 24 IS - SP - 305 EP - 339 UR - http://dblp.uni-trier.de/db/journals/jair/jair24.html#CimianoHS05 DO - KW - 2005 KW - fca KW - folksonomy KW - hierarchies KW - hierarchy KW - learning KW - myown KW - ontologies KW - text L1 - SN - N1 - N1 - AB - ER - TY - THES AU - Böttger, Sebastian T1 - Konzept und Umsetzung eines Tag-Recommenders für Video-Ressourcen am Beispiel UniVideo PY - 2012/04 PB - Universität Kassel SP - EP - UR - http://www.uni-kassel.de/~seboettg/ba-thesis.pdf DO - KW - ba-thesis KW - bathesis KW - folksonomy KW - myown KW - recommender KW - tagging KW - tagrecommendation KW - thesis KW - univideo KW - video L1 - N1 - N1 - AB - 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. ER - TY - CONF AU - Hotho, Andreas AU - J?schke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Sure, York A2 - Domingue, John T1 - Information Retrieval in Folksonomies: Search and Ranking T2 - The Semantic Web: Research and Applications PB - Springer C1 - Heidelberg PY - 2006/06 CY - VL - 4011 IS - SP - 411 EP - 426 UR - DO - KW - 2006 KW - FCA KW - IR KW - OntologyHandbook KW - folkrank KW - folksonomy KW - information KW - informationretrieval KW - itegpub KW - mimose KW - myown KW - pagerank KW - ranking KW - retrieval L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Sure, York A2 - Domingue, John T1 - Information Retrieval in Folksonomies: Search and Ranking T2 - The Semantic Web: Research and Applications PB - Springer C1 - Heidelberg PY - 2006/06 CY - VL - 4011 IS - SP - 411 EP - 426 UR - DO - KW - 2006 KW - FCA KW - IR KW - OntologyHandbook KW - folkrank KW - folksonomy KW - information KW - informationretrieval KW - itegpub KW - mimose KW - myown KW - pagerank KW - ranking KW - retrieval L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Abrams, David AU - Baecker, Ron AU - Chignell, Mark A2 - T1 - Information archiving with bookmarks: personal Web space construction and organization T2 - Proceedings of the SIGCHI Conference on Human Factors in Computing Systems PB - ACM Press/Addison-Wesley Publishing Co. C1 - New York, NY, USA PY - 1998/ CY - VL - IS - SP - 41 EP - 48 UR - http://dx.doi.org/10.1145/274644.274651 DO - 10.1145/274644.274651 KW - analysis KW - bookmarking KW - folksonomy KW - log KW - social KW - usage L1 - SN - 0-201-30987-4 N1 - Information archiving with bookmarks N1 - AB - ER - TY - CHAP AU - Wartena, Christian AU - Wibbels, Martin A2 - Clough, Paul A2 - Foley, Colum A2 - Gurrin, Cathal A2 - Jones, Gareth A2 - Kraaij, Wessel A2 - Lee, Hyowon A2 - Mudoch, Vanessa T1 - Improving Tag-Based Recommendation by Topic Diversification T2 - Advances in Information Retrieval PB - Springer C1 - Berlin/Heidelberg PY - 2011/ VL - 6611 IS - SP - 43 EP - 54 UR - http://dx.doi.org/10.1007/978-3-642-20161-5_7 DO - 10.1007/978-3-642-20161-5_7 KW - folksonomy KW - item KW - recommender KW - social KW - tagging KW - topic L1 - SN - 978-3-642-20160-8 N1 - N1 - AB - 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. ER - TY - CONF AU - Wetzker, Robert AU - Zimmermann, Carsten AU - Bauckhage, Christian AU - Albayrak, Sahin A2 - T1 - I tag, you tag: translating tags for advanced user models T2 - Proceedings of the third ACM international conference on Web search and data mining PB - ACM C1 - New York, NY, USA PY - 2010/ CY - VL - IS - SP - 71 EP - 80 UR - http://doi.acm.org/10.1145/1718487.1718497 DO - 10.1145/1718487.1718497 KW - folksonomy KW - model KW - recommender KW - tagging KW - taggingsurvey L1 - SN - 978-1-60558-889-6 N1 - I tag, you tag N1 - AB - 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. ER - TY - JOUR AU - Zubiaga, Arkaitz AU - Fresno, Victor AU - Martinez, Raquel AU - Garcia-Plaza, Alberto P. T1 - Harnessing Folksonomies to Produce a Social Classification of Resources JO - IEEE Transactions on Knowledge and Data Engineering PY - 2012/ VL - 99 IS - PrePrints SP - EP - UR - DO - http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.115 KW - classification KW - delicious KW - folksonomy KW - tagging KW - toread KW - dataset L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Domnguez Garca, Renato AU - Bender, Matthias AU - Anjorin, Mojisola AU - Rensing, Christoph AU - Steinmetz, Ralf A2 - T1 - FReSET: an evaluation framework for folksonomy-based recommender systems T2 - Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web PB - ACM C1 - New York, NY, USA PY - 2012/ CY - VL - IS - SP - 25 EP - 28 UR - http://doi.acm.org/10.1145/2365934.2365939 DO - 10.1145/2365934.2365939 KW - evaluation KW - folksonomy KW - framework KW - freset L1 - SN - 978-1-4503-1638-5 N1 - FReSET N1 - AB - 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. ER - TY - THES AU - Jäschke, Robert T1 - Formal concept analysis and tag recommendations in collaborative tagging systems PY - 2011/ PB - SP - EP - UR - http://opac.bibliothek.uni-kassel.de/DB=1/PPN?PPN=231779038 DO - KW - baarbeit KW - folksonomy KW - social KW - tagging KW - toread L1 - N1 - UB Kassel N1 - AB - ER - TY - THES AU - Jäschke, Robert T1 - Formal concept analysis and tag recommendations in collaborative tagging systems PY - 2011/ PB - SP - EP - UR - http://opac.bibliothek.uni-kassel.de/DB=1/PPN?PPN=231779038 DO - KW - baarbeit KW - folksonomy KW - recommender KW - social_tagging KW - toread L1 - N1 - UB Kassel N1 - AB - ER - TY - CHAP AU - Singer, Philipp AU - Niebler, Thomas AU - Hotho, Andreas AU - Strohmaier, Markus A2 - T1 - Folksonomies T2 - Encyclopedia of Social Network Analysis and Mining PB - Springer C1 - PY - 2014/ VL - IS - SP - 542 EP - 547 UR - DO - KW - 2014 KW - characterization KW - folksonomy KW - myown L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Gwizdka, Jacek AU - Cole, Michael A2 - Kovács, László A2 - Fuhr, Norbert A2 - Meghini, Carlo T1 - Finding It on Google, Finding It on del.icio.us. T2 - ECDL PB - Springer C1 - PY - 2007/ CY - VL - 4675 IS - SP - 559 EP - 562 UR - http://dblp.uni-trier.de/db/conf/ercimdl/ecdl2007.html#GwizdkaC07 DO - KW - search KW - logsonomy KW - levsem09 KW - folksonomy L1 - SN - 978-3-540-74850-2 N1 - dblp N1 - AB - ER - TY - CONF AU - Landia, Nikolas AU - Anand, Sarabjot Singh AU - Hotho, Andreas AU - Jäschke, Robert AU - Doerfel, Stephan AU - Mitzlaff, Folke A2 - T1 - Extending FolkRank with content data T2 - Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web PB - ACM C1 - New York, NY, USA PY - 2012/ CY - VL - IS - SP - 1 EP - 8 UR - http://doi.acm.org/10.1145/2365934.2365936 DO - 10.1145/2365934.2365936 KW - 2012 KW - bookmarking KW - folkrank KW - folksonomy KW - myown KW - social KW - tagging L1 - SN - 978-1-4503-1638-5 N1 - Extending FolkRank with content data N1 - AB - 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.
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. ER - TY - JOUR AU - Yu, L. AU - Li, Q. AU - Xie, H. AU - Cai, Y. T1 - Exploring Folksonomy and Cooking Procedures to Boost Cooking Recipe Recommendation JO - Web Technologies and Applications PY - 2011/ VL - IS - SP - 119 EP - 130 UR - DO - KW - application KW - cooking KW - folksonomy L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Parra, Denis AU - Brusilovsky, Peter A2 - T1 - Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike T2 - Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web PB - C1 - PY - 2009/06 CY - VL - 467 IS - SP - EP - UR - http://ceur-ws.org/Vol-467/paper5.pdf DO - KW - collaborative KW - filtering KW - folksonomy KW - item KW - recommender KW - social KW - tagging L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Hochberger, Christian A2 - Liskowsky, Rüdiger T1 - Emergent Semantics in BibSonomy T2 - Informatik 2006 -- Informatik für Menschen. Band 2 PB - Gesellschaft für Informatik C1 - Bonn PY - 2006/october CY - VL - P-94 IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006emergent.pdf DO - KW - 2006 KW - UniK KW - bibsonomy KW - emergence KW - emergent KW - emergentsemantics_evidence KW - folksonomy KW - hotho KW - itegpub KW - jaeschke KW - l3s KW - myown KW - nepomuk KW - ol_web2.0 KW - schmitz KW - semantics KW - stumme KW - tagorapub L1 - SN - N1 - N1 - AB - 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. ER - TY - JOUR AU - Landia, Nikolas AU - Doerfel, Stephan AU - Jäschke, Robert AU - Anand, Sarabjot Singh AU - Hotho, Andreas AU - Griffiths, Nathan T1 - Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations JO - cs.IR PY - 2013/ VL - 1310.1498 IS - SP - EP - UR - http://arxiv.org/abs/1310.1498 DO - KW - 2013 KW - bookmarking KW - collaborative KW - folkrank KW - folksonomy KW - graph KW - iteg KW - itegpub KW - l3s KW - recommender KW - social KW - tagging L1 - SN - N1 - N1 - AB - 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. ER - TY - JOUR AU - Landia, Nikolas AU - Doerfel, Stephan AU - Jäschke, Robert AU - Anand, Sarabjot Singh AU - Hotho, Andreas AU - Griffiths, Nathan T1 - Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations JO - cs.IR PY - 2013/ VL - 1310.1498 IS - SP - EP - UR - http://arxiv.org/abs/1310.1498 DO - KW - 2013 KW - bookmarking KW - collaborative KW - folkrank KW - folksonomy KW - graph KW - myown L1 - SN - N1 - N1 - AB - 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. ER - TY - JOUR AU - Landia, Nikolas AU - Doerfel, Stephan AU - Jäschke, Robert AU - Anand, Sarabjot Singh AU - Hotho, Andreas AU - Griffiths, Nathan T1 - Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations JO - cs.IR PY - 2013/ VL - 1310.1498 IS - SP - EP - UR - http://arxiv.org/abs/1310.1498 DO - KW - 2013 KW - bookmarking KW - collaborative KW - folkrank KW - folksonomy KW - graph KW - myown KW - recommender KW - social KW - tagging L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Szomszor, Martin AU - Cantador, Iván AU - Alani, Harith A2 - T1 - Correlating user profiles from multiple folksonomies T2 - Hypertext PB - C1 - PY - 2008/ CY - VL - IS - SP - 33 EP - 42 UR - DO - KW - matching KW - folksonomy KW - user_profiles KW - user KW - profile L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Grahl, Miranda AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Conceptual Clustering of Social Bookmarking Sites T2 - 7th International Conference on Knowledge Management (I-KNOW '07) PB - Know-Center C1 - Graz, Austria PY - 2007/10 CY - VL - IS - SP - 356 EP - 364 UR - http://www.tagora-project.eu/wp-content/2007/06/grahl_iknow07.pdf DO - KW - 2007 KW - folksonomies KW - folksonomy KW - itegpub KW - myown KW - sites KW - social KW - tagging KW - tagorapub L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Grahl, Miranda AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Conceptual Clustering of Social Bookmarking Sites T2 - 7th International Conference on Knowledge Management (I-KNOW '07) PB - Know-Center C1 - Graz, Austria PY - 2007/10 CY - VL - IS - SP - 356 EP - 364 UR - http://www.tagora-project.eu/wp-content/2007/06/grahl_iknow07.pdf DO - KW - sites KW - 2007 KW - tagging KW - social KW - folksonomy KW - tagorapub KW - folksonomies KW - itegpub L1 - SN - N1 - N1 - AB - 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. ER - TY - CONF AU - Grahl, Miranda AU - Hotho, Andreas AU - Stumme, Gerd A2 - Hinneburg, Alexander T1 - Conceptual Clustering of Social Bookmark Sites T2 - Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007) PB - Martin-Luther-Universität Halle-Wittenberg C1 - PY - 2007/10 CY - VL - IS - SP - 50 EP - 54 UR - http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf DO - KW - 2007 KW - Social KW - bookmark KW - bookmarking KW - clustering KW - collaborative KW - conceptual KW - folksonomies KW - folksonomy KW - itegpub KW - myown KW - social KW - tagging KW - tagorapub L1 - SN - 978-3-86010-907-6 N1 - N1 - AB - ER - TY - JOUR AU - Zhang, Yin AU - Zhang, Bin AU - Gao, Kening AU - Guo, Pengwei AU - Sun, Daming T1 - Combining content and relation analysis for recommendation in social tagging systems JO - Physica A: Statistical Mechanics and its Applications PY - 2012/ VL - 391 IS - 22 SP - 5759 EP - 5768 UR - http://www.sciencedirect.com/science/article/pii/S0378437112003846 DO - 10.1016/j.physa.2012.05.013 KW - folkrank KW - folksonomy KW - lda KW - model KW - ranking KW - toread L1 - SN - N1 - ScienceDirect.com - Physica A: Statistical Mechanics and its Applications - Combining content and relation analysis for recommendation in social tagging systems N1 - AB - 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. ER - TY - CONF AU - Angelova, Ralitsa AU - Lipczak, Marek AU - Milios, Evangelos AU - Prałat, Paweł A2 - T1 - Characterizing a social bookmarking and tagging network T2 - Proceedings of the Mining Social Data Workshop (MSoDa) PB - C1 - PY - 2008/07 CY - VL - IS - SP - 21 EP - 25 UR - http://www.math.ryerson.ca/~pralat/papers/2008_delicious.pdf DO - KW - analysis KW - bookmarking KW - collaborative KW - folksonomy KW - network KW - tagging L1 - SN - N1 - N1 - AB - 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. ER - TY - CHAP AU - Jäschke, Robert AU - Hotho, Andreas AU - Mitzlaff, Folke AU - Stumme, Gerd A2 - Pazos Arias, José J. A2 - Fernández Vilas, Ana A2 - Díaz Redondo, Rebeca P. T1 - Challenges in Tag Recommendations for Collaborative Tagging Systems T2 - Recommender Systems for the Social Web PB - Springer C1 - Berlin/Heidelberg PY - 2012/ VL - 32 IS - SP - 65 EP - 87 UR - http://dx.doi.org/10.1007/978-3-642-25694-3_3 DO - 10.1007/978-3-642-25694-3_3 KW - 2012 KW - bookmarking KW - challenge KW - collaborative KW - dc09 KW - discovery KW - folksonomy KW - myown KW - recommender KW - rsdc08 KW - social KW - tagging L1 - SN - 978-3-642-25694-3 N1 - N1 - AB - 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. ER - TY - CHAP AU - Jäschke, Robert AU - Hotho, Andreas AU - Mitzlaff, Folke AU - Stumme, Gerd A2 - Pazos Arias, José J. A2 - Fernández Vilas, Ana A2 - Díaz Redondo, Rebeca P. T1 - Challenges in Tag Recommendations for Collaborative Tagging Systems T2 - Recommender Systems for the Social Web PB - Springer C1 - Berlin/Heidelberg PY - 2012/ VL - 32 IS - SP - 65 EP - 87 UR - http://dx.doi.org/10.1007/978-3-642-25694-3_3 DO - 10.1007/978-3-642-25694-3_3 KW - 2012 KW - bookmarking KW - challenge KW - collaborative KW - dc09 KW - discovery KW - folksonomy KW - info20 KW - itegpub KW - l3s KW - myown KW - recommender KW - rsdc08 KW - social KW - tagging L1 - SN - 978-3-642-25694-3 N1 - N1 - AB - 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. ER - TY - CHAP AU - Jäschke, Robert AU - Hotho, Andreas AU - Mitzlaff, Folke AU - Stumme, Gerd A2 - Pazos Arias, José J. A2 - Fernández Vilas, Ana A2 - Díaz Redondo, Rebeca P. T1 - Challenges in Tag Recommendations for Collaborative Tagging Systems T2 - Recommender Systems for the Social Web PB - Springer C1 - Berlin/Heidelberg PY - 2012/ VL - 32 IS - SP - 65 EP - 87 UR - http://dx.doi.org/10.1007/978-3-642-25694-3_3 DO - 10.1007/978-3-642-25694-3_3 KW - 2012 KW - bookmarking KW - challenge KW - collaborative KW - dc09 KW - discovery KW - folksonomy KW - myown KW - recommender KW - rsdc08 KW - social KW - tagging L1 - SN - 978-3-642-25694-3 N1 - N1 - AB - 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. ER - TY - CONF AU - Yanbe, Yusuke AU - Jatowt, Adam AU - Nakamura, Satoshi AU - Tanaka, Katsumi A2 - T1 - Can social bookmarking enhance search in the web? T2 - JCDL '07: Proceedings of the 2007 conference on Digital libraries PB - ACM Press C1 - New York, NY, USA PY - 2007/ CY - VL - IS - SP - 107 EP - 116 UR - http://portal.acm.org/citation.cfm?id=1255175.1255198 DO - http://doi.acm.org/10.1145/1255175.1255198 KW - search KW - logsonomy KW - social_bookmarking KW - folksonomy L1 - SN - 978-1-59593-644-8 N1 - Can social bookmarking enhance search in the web? N1 - AB - ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - de Moor, Aldo A2 - Polovina, Simon A2 - Delugach, Harry T1 - BibSonomy: A Social Bookmark and Publication Sharing System T2 - Proceedings of the First Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures PB - Aalborg Universitetsforlag C1 - Aalborg PY - 2006/ CY - VL - IS - SP - 87 EP - 102 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006bibsonomy.pdf DO - KW - 2006 KW - bookmarking KW - social KW - nepomuk KW - bibsonomy KW - OntologyHandbook KW - FCA KW - folksonomy KW - tagorapub KW - iccs KW - l3s L1 - SN - 87-7307-769-0 N1 - N1 - AB - 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. ER - TY - JOUR AU - Peterson, Elaine T1 - Beneath the Metadata: Some Philosophical Problems with Folksonomy JO - D-Lib Magazine PY - 2006/november VL - 12 IS - 11 SP - EP - UR - http://www.dlib.org/dlib/november06/peterson/11peterson.html DO - 10.1045/november2006-peterson KW - folksonomy KW - metadata KW - philosophy L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Peterson, Elaine T1 - Beneath the Metadata: Some Philosophical Problems with Folksonomy JO - D-Lib Magazine PY - 2006/november VL - 12 IS - 11 SP - EP - UR - http://www.dlib.org/dlib/november06/peterson/11peterson.html DO - 10.1045/november2006-peterson KW - folksonomies KW - folksonomy KW - relativism KW - tagging L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Aurnhammer, Melanie AU - Hanappe, Peter AU - Steels, Luc A2 - T1 - Augmenting Navigation for Collaborative Tagging with Emergent Semantics T2 - PB - C1 - PY - 2006/ CY - VL - 4273 IS - SP - 58 EP - 71 UR - http://dx.doi.org/10.1007/11926078_5 DO - KW - folksonomy KW - semantics KW - ol_web2.0 KW - emergentsemantics_evidence L1 - SN - N1 - SpringerLink - Buchkapitel N1 - AB - 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. ER - TY - CONF AU - Jäschke, Robert AU - Hotho, Andreas AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Priss, U. A2 - Polovina, S. A2 - Hill, R. T1 - Analysis of the Publication Sharing Behaviour in BibSonomy T2 - Proceedings of the 15th International Conference on Conceptual Structures (ICCS 2007) PB - Springer-Verlag C1 - Berlin, Heidelberg PY - 2007/07 CY - VL - 4604 IS - SP - 283 EP - 295 UR - DO - KW - 2007 KW - BibSonomy KW - bibsonomy KW - bookmarking KW - fca KW - folksonomy KW - iccs KW - itegpub KW - l3s KW - myown KW - publication KW - sharing KW - social KW - trias L1 - SN - 3-540-73680-8 N1 - N1 - AB - 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. ER - TY - CHAP AU - Lorince, Jared AU - Joseph, Kenneth AU - Todd, PeterM. A2 - Agarwal, Nitin A2 - Xu, Kevin A2 - Osgood, Nathaniel T1 - Analysis of Music Tagging and Listening Patterns: Do Tags Really Function as Retrieval Aids? T2 - Social Computing, Behavioral-Cultural Modeling, and Prediction PB - Springer International Publishing C1 - PY - 2015/ VL - 9021 IS - SP - 141 EP - 152 UR - http://dx.doi.org/10.1007/978-3-319-16268-3_15 DO - 10.1007/978-3-319-16268-3_15 KW - folksonomy KW - last.fm KW - retrieval KW - tagging KW - usage L1 - SN - 978-3-319-16267-6 N1 - Analysis of Music Tagging and Listening Patterns: Do Tags Really Function as Retrieval Aids? - Springer N1 - AB - 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 ER - TY - CONF AU - Zhou, Mianwei AU - Bao, Shenghua AU - Wu, Xian AU - Yu, Yong A2 - T1 - An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations T2 - PB - C1 - PY - 2008/ CY - VL - IS - SP - 680 EP - 693 UR - http://dx.doi.org/10.1007/978-3-540-76298-0_49 DO - KW - folksonomy KW - learning KW - semantics KW - ol_web2.0 KW - methods_concepthierarchy KW - data_tagging L1 - SN - N1 - SpringerLink - Book Chapter N1 - AB - 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. ER - TY - JOUR AU - Gasevic, Dragan AU - Zouaq, Amal AU - Torniai, Carlo AU - Jovanovic, Jelena AU - Hatala, Marek T1 - An Approach to Folksonomy-based Ontology Maintenance for Learning Environments JO - IEEE Transactions on Learning Technologies PY - 2011/ VL - 99 IS - 1 SP - EP - UR - http://www.computer.org/portal/web/csdl/doi/10.1109/TLT.2011.21 DO - 10.1109/TLT.2011.21 KW - folksonomy KW - maintenance KW - ontology KW - tags L1 - SN - N1 - An Approach to Folksonomy-based Ontology Maintenance for Learning Environments N1 - AB - ER - TY - CONF AU - Doerfel, Stephan AU - Jäschke, Robert A2 - T1 - An analysis of tag-recommender evaluation procedures T2 - Proceedings of the 7th ACM conference on Recommender systems PB - ACM C1 - New York, NY, USA PY - 2013/ CY - VL - IS - SP - 343 EP - 346 UR - https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf DO - 10.1145/2507157.2507222 KW - 2013 KW - bibsonomy KW - bookmarking KW - collaborative KW - core KW - evaluation KW - folkrank KW - folksonomy KW - graph KW - iteg KW - itegpub KW - l3s KW - recommender KW - social KW - tagging L1 - SN - 978-1-4503-2409-0 N1 - N1 - AB - 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. ER - TY - CONF AU - Plangprasopchok, Anon AU - Lerman, Kristina AU - Getoor, Lise A2 - T1 - A Probabilistic Approach for Learning Folksonomies from Structured Data T2 - Proceedings of the 4th ACM Web Search and Data Mining Conference PB - C1 - PY - 2010/ CY - VL - IS - SP - EP - UR - http://arxiv.org/abs/1011.3557 DO - KW - affinity_propagation KW - deletethistag KW - folksonomy KW - learning KW - ontology L1 - SN - N1 - A Probabilistic Approach for Learning Folksonomies from Structured Data N1 - AB - 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. ER - TY - CONF AU - Pera, Maria Soledad AU - Ng, Yiu-Kai A2 - T1 - A personalized recommendation system on scholarly publications T2 - Proceedings of the 20th ACM international conference on Information and knowledge management PB - ACM C1 - New York, NY, USA PY - 2011/ CY - VL - IS - SP - 2133 EP - 2136 UR - http://doi.acm.org/10.1145/2063576.2063908 DO - 10.1145/2063576.2063908 KW - folksonomy KW - item KW - recommender KW - social KW - tagging L1 - SN - 978-1-4503-0717-8 N1 - N1 - AB - 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. ER - TY - CONF AU - Wetzker, Robert AU - Umbrath, Winfried AU - Said, Alan A2 - T1 - A hybrid approach to item recommendation in folksonomies T2 - Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval PB - ACM C1 - New York, NY, USA PY - 2009/ CY - VL - IS - SP - 25 EP - 29 UR - http://doi.acm.org/10.1145/1506250.1506255 DO - 10.1145/1506250.1506255 KW - folksonomy KW - itemRecommendation KW - plsa L1 - SN - 978-1-60558-430-0 N1 - A hybrid approach to item recommendation in folksonomies N1 - AB - 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 delicious bookmarking service show the scalability of our approach and an improved recommendation quality compared to two-mode collaborative or annotation based methods. ER - TY - CHAP AU - Solskinnsbakk, Geir AU - Gulla, Jon A2 - Meersman, Robert A2 - Dillon, Tharam A2 - Herrero, Pilar T1 - A Hybrid Approach to Constructing Tag Hierarchies T2 - On the Move to Meaningful Internet Systems, OTM 2010 PB - Springer C1 - Berlin / Heidelberg PY - 2010/ VL - 6427 IS - SP - 975 EP - 982 UR - http://dx.doi.org/10.1007/978-3-642-16949-6_22 DO - 10.1007/978-3-642-16949-6_22 KW - folksonomy KW - learning KW - ontology L1 - SN - 978-3-642-16948-9 N1 - SpringerLink - Abstract N1 - AB - 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. ER - TY - CONF AU - Krause, Beate AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - A comparison of social bookmarking with traditional search T2 - Proceedings of the IR research, 30th European conference on Advances in information retrieval PB - Springer-Verlag C1 - Berlin, Heidelberg PY - 2008/ CY - VL - IS - SP - 101 EP - 113 UR - http://dl.acm.org/citation.cfm?id=1793274.1793290 DO - KW - bookmarking KW - comparison KW - folksonomy KW - ranking KW - search KW - social L1 - SN - 3-540-78645-7, 978-3-540-78645-0 N1 - A comparison of social bookmarking with traditional search N1 - AB - 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.
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.
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. ER - TY - CONF AU - Illig, Jens AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - Wolff, Karl Erich A2 - Palchunov, Dmitry E. A2 - Zagoruiko, Nikolay G. A2 - Andelfinger, Urs T1 - A Comparison of Content-Based Tag Recommendations in Folksonomy Systems T2 - Knowledge Processing and Data Analysis PB - Springer C1 - Berlin/Heidelberg PY - 2011/ CY - VL - 6581 IS - SP - 136 EP - 149 UR - http://dx.doi.org/10.1007/978-3-642-22140-8_9 DO - 10.1007/978-3-642-22140-8_9 KW - 2011 KW - content KW - folksonomy KW - myown KW - recommendations KW - recommender KW - tag L1 - SN - 978-3-642-22139-2 N1 - N1 - AB - 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. ER - TY - CONF AU - Illig, Jens AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - Wolff, Karl Erich A2 - Palchunov, Dmitry E. A2 - Zagoruiko, Nikolay G. A2 - Andelfinger, Urs T1 - A Comparison of Content-Based Tag Recommendations in Folksonomy Systems T2 - Knowledge Processing and Data Analysis PB - Springer C1 - Berlin/Heidelberg PY - 2011/ CY - VL - 6581 IS - SP - 136 EP - 149 UR - http://dx.doi.org/10.1007/978-3-642-22140-8_9 DO - 10.1007/978-3-642-22140-8_9 KW - 2011 KW - content KW - folksonomy KW - info20 KW - itegpub KW - l3s KW - myown KW - recommendations KW - recommender KW - tag KW - tagorapub L1 - SN - 978-3-642-22139-2 N1 - N1 - AB - 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.
ER - TY - CONF AU - Illig, Jens AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - Wolff, Karl Erich A2 - Palchunov, Dmitry E. A2 - Zagoruiko, Nikolay G. A2 - Andelfinger, Urs T1 - A Comparison of Content-Based Tag Recommendations in Folksonomy Systems T2 - Knowledge Processing and Data Analysis PB - Springer C1 - Berlin/Heidelberg PY - 2011/ CY - VL - 6581 IS - SP - 136 EP - 149 UR - http://dx.doi.org/10.1007/978-3-642-22140-8_9 DO - 10.1007/978-3-642-22140-8_9 KW - 2011 KW - comparison KW - content KW - folksonomy KW - recommendations L1 - SN - 978-3-642-22139-2 N1 - N1 - AB - 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. ER - TY - CONF AU - Illig, Jens AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - T1 - A Comparison of content-based Tag Recommendations in Folksonomy Systems T2 - Postproceedings of the International Conference on Knowledge Processing in Practice (KPP 2007) PB - Springer C1 - PY - 2011/ CY - VL - IS - SP - EP - UR - DO - KW - 2011 KW - content KW - folksonomy KW - itegpub KW - l3s KW - myown KW - recommendations KW - recommender KW - tag KW - tagorapub L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Illig, Jens AU - Hotho, Andreas AU - Jäschke, Robert AU - Stumme, Gerd A2 - T1 - A Comparison of content-based Tag Recommendations in Folksonomy Systems T2 - Postproceedings of the International Conference on Knowledge Processing in Practice (KPP2007) PB - Springer C1 - PY - to appear/ CY - VL - IS - SP - EP - UR - DO - KW - content KW - 2009 KW - nopaper KW - tag KW - recommender KW - recommendations KW - folksonomy KW - tagorapub L1 - SN - N1 - N1 - AB - ER -