@article{landia2013deeper, abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.}, author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan}, interhash = {e8095b13630452ce3ecbae582f32f4bc}, intrahash = {e585a92994be476480545eb62d741642}, journal = {cs.IR}, title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations}, url = {http://arxiv.org/abs/1310.1498}, volume = {1310.1498}, year = 2013 } @inproceedings{doerfel2013analysis, abstract = {Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.}, acmid = {2507222}, address = {New York, NY, USA}, author = {Doerfel, Stephan and Jäschke, Robert}, booktitle = {Proceedings of the 7th ACM conference on Recommender systems}, doi = {10.1145/2507157.2507222}, interhash = {3eaf2beb1cdad39b7c5735a82c3338dd}, intrahash = {aa4b3d79a362d7415aaa77625b590dfa}, isbn = {978-1-4503-2409-0}, location = {Hong Kong, China}, numpages = {4}, pages = {343--346}, publisher = {ACM}, series = {RecSys '13}, title = {An analysis of tag-recommender evaluation procedures}, url = {https://www.kde.cs.uni-kassel.de/pub/pdf/doerfel2013analysis.pdf}, year = 2013 } @book{balbymarinho2012recommender, abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.}, author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.}, doi = {10.1007/978-1-4614-1894-8}, interhash = {0bb7f0588cd690d67cc73e219a3a24fa}, intrahash = {87d6883ebd98e8810be45d7e7e4ade96}, isbn = {978-1-4614-1893-1}, month = feb, publisher = {Springer}, series = {SpringerBriefs in Electrical and Computer Engineering}, title = {Recommender Systems for Social Tagging Systems}, url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8}, year = 2012 } @inproceedings{doerfel2012leveraging, abstract = {The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.}, address = {New York, NY, USA}, author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, doi = {10.1145/2365934.2365937}, interhash = {beb2c81daf975eeed6e01e1b412196b1}, intrahash = {64bf590675a833770b7d284871435a8d}, isbn = {978-1-4503-1638-5}, location = {Dublin, Ireland}, month = sep, pages = {9--16}, publisher = {ACM}, title = {Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation }, url = {http://doi.acm.org/10.1145/2365934.2365937}, year = 2012 } @incollection{jaeschke2012challenges, abstract = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.}, address = {Berlin/Heidelberg}, affiliation = {Knowledge & Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany}, author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, booktitle = {Recommender Systems for the Social Web}, doi = {10.1007/978-3-642-25694-3_3}, editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.}, interhash = {75b1a6f54ef54d0126d0616b5bf77563}, intrahash = {7d41d332cccc3e7ba8e7dadfb7996337}, isbn = {978-3-642-25694-3}, pages = {65--87}, publisher = {Springer}, series = {Intelligent Systems Reference Library}, title = {Challenges in Tag Recommendations for Collaborative Tagging Systems}, url = {http://dx.doi.org/10.1007/978-3-642-25694-3_3}, volume = 32, year = 2012 } @incollection{marinho2011social, abstract = {The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. 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 STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.}, address = {New York}, author = {Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis}, booktitle = {Recommender Systems Handbook}, doi = {10.1007/978-0-387-85820-3_19}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, interhash = {2d4afa6f7fb103ccc166c9c5d629cdd1}, intrahash = {708be7b5c269bd3a9d3d2334f858d52d}, isbn = {978-0-387-85820-3}, pages = {615--644}, publisher = {Springer}, title = {Social Tagging Recommender Systems}, url = {http://dx.doi.org/10.1007/978-0-387-85820-3_19}, year = 2011 } @proceedings{alani2007workshop, address = {Banff, Canada}, editor = {Alani, Harith and Noy, Natasha and Stumme, Gerd and Mika, Peter and Sure, York and Vrandecic, Denny}, interhash = {377c136010d79673ae5aaf7cf0cd84cb}, intrahash = {aac9110f5a4e2e00028f0466555ce8d4}, title = {Workshop on Social and Collaborative Construction of Structured Knowledge (CKC 2007) at WWW 2007}, url = {http://www2007.org/workshop-W7.php}, year = 2007 } @inproceedings{jaeschke07tagKdml, author = {Jaeschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, booktitle = {Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)}, editor = {Hinneburg, Alexander}, interhash = {19e40fd1eb137fab091512656ecc504d}, intrahash = {71bc9f8ae1a53632dc9a2b98b017f152}, isbn = {978-3-86010-907-6}, month = sep, pages = {13-20}, publisher = {Martin-Luther-Universität Halle-Wittenberg}, title = {Tag Recommendations in Folksonomies}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2007/jaeschke07tagrecommendationsKDML.pdf}, year = 2007 } @inproceedings{cattuto08-semantic, abstract = {Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.}, address = {Patras, Greece}, author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3)}, interhash = {cc62b733f6e0402db966d6dbf1b7711f}, intrahash = {3b0aca61b24e4343bd80390614e3066e}, month = {July}, title = {Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems}, url = {http://olp.dfki.de/olp3/}, year = 2008 } @inproceedings{grahl07conceptualKdml, author = {Grahl, Miranda and Hotho, Andreas and Stumme, Gerd}, booktitle = {Workshop Proceedings of Lernen -- Wissensentdeckung -- Adaptivität (LWA 2007)}, editor = {Hinneburg, Alexander}, interhash = {9c3bb05456bf11bcd88a1135de51f7d9}, intrahash = {6d5188d66564fe4ed7386e28868504de}, isbn = {978-3-86010-907-6}, month = sep, pages = {50-54}, publisher = {Martin-Luther-Universität Halle-Wittenberg}, title = {Conceptual Clustering of Social Bookmark Sites}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2007/kdml_recommender_final.pdf}, vgwort = {14}, year = 2007 } @inproceedings{paper:lambiotte:2006, abstract = {We describe online collaborative communities by tripartite networks, the nodes being persons, items and tags. We introduce projection methods in order to uncover the structures of the networks, i.e. communities of users, genre families... The structuring of the network is visualised by using a tree representation. The notion of diversity in the system is also discussed.}, author = {Lambiotte, Renaud and Ausloos, Marcel}, booktitle = {Computational Science – ICCS 2006}, interhash = {1ed3cd51137e724355234662fac3bdda}, intrahash = {71f0fcb3b9b1cbe601da92fd3bf7ce60}, pages = {1114-1117}, publisher = {Springer Berlin / Heidelberg}, title = {Collaborative Tagging as a Tripartite Network}, year = 2006 } @article{benz07-supporting, author = {Benz, Dominik and Tso, Karen H. L. and Schmidt-Thieme, Lars}, interhash = {5feb6b95a7217fe0616c3fff4a2f0169}, intrahash = {aad3ecd17f5b738a7035c9f37ffe988d}, journal = {Special Issue of the Computer Networks journal on Innovations in Web Communications Infrastructure}, title = {Suppporting Collaborative Hierarchical Classification: Bookmarks as an Example}, year = 2007 } @inbook{schmitz2006kollaboratives, abstract = {Wissensmanagement in zentralisierten Wissensbasen erfordert einen hohen Aufwand für Erstellung und Wartung, und es entspricht nicht immer den Anforderungen der Benutzer. Wir geben in diesem Kapitel einen Überblick über zwei aktuelle Ansätze, die durch kollaboratives Wissensmanagement diese Probleme lösen können. Im Peer-to-Peer-Wissensmanagement unterhalten Benutzer dezentrale Wissensbasen, die dann vernetzt werden können, um andere Benutzer eigene Inhalte nutzen zu lassen. Folksonomies versprechen, die Wissensakquisition so einfach wie möglich zu gestalten und so viele Benutzer in den Aufbau und die Pflege einer gemeinsamen Wissensbasis einzubeziehen.}, author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, booktitle = {Semantic Web - Wege zur vernetzten Wissensgesellschaft}, editor = {Pellegrini, Tassilo and Blumauer, Andreas}, interhash = {cc0f3d4fa8f36968f02837e3f9f5c57b}, intrahash = {53e13744981f2c04d9239e0cf9b4e689}, isbn = {3-540-29324-8}, pages = {273-290}, publisher = {Springer}, title = {Kollaboratives Wissensmanagement}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006kollaboratives.pdf}, year = 2006 } @article{245123, address = {New York, NY, USA}, author = {Kautz, Henry and Selman, Bart and Shah, Mehul}, doi = {http://doi.acm.org/10.1145/245108.245123}, interhash = {6995678b936b33eef9ea1396e53a1fc7}, intrahash = {ba3606b3aa6c4cf94784db451b28cd68}, issn = {0001-0782}, journal = {Commun. ACM}, number = 3, pages = {63--65}, publisher = {ACM Press}, title = {Referral Web: combining social networks and collaborative filtering}, volume = 40, year = 1997 }