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

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

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

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.

@inproceedings{doerfel2012leveraging, author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd}, title = {Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation }, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, publisher = {ACM}, address = {New York, NY, USA}, year = {2012}, pages = {9--16}, url = {http://doi.acm.org/10.1145/2365934.2365937}, doi = {10.1145/2365934.2365937}, isbn = {978-1-4503-1638-5}, keywords = {2012, bookmarking, collaborative, folkrank, itegpub, l3s, myown, recommender, social, tagging}, 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.} }

%0 = inproceedings %A = Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web %C = New York, NY, USA %D = 2012 %I = ACM %T = Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation %U = http://doi.acm.org/10.1145/2365934.2365937

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

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

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

@article{journals/insk/KrauseLHRS12, author = {Krause, Beate and Lerch, Hana and Hotho, Andreas and Roßnagel, Alexander and Stumme, Gerd}, title = {Datenschutz im Web 2.0 am Beispiel des sozialen Tagging-Systems BibSonomy.}, journal = {Informatik Spektrum}, year = {2012}, volume = {35}, number = {1}, pages = {12-23}, url = {http://dblp.uni-trier.de/db/journals/insk/insk35.html#KrauseLHRS12}, keywords = {2012, bibsonomy, datenschutz, info20, itegpub, l3s, myown, privacy, tagging} }

%0 = article %A = Krause, Beate and Lerch, Hana and Hotho, Andreas and Roßnagel, Alexander and Stumme, Gerd %D = 2012 %T = Datenschutz im Web 2.0 am Beispiel des sozialen Tagging-Systems BibSonomy. %U = http://dblp.uni-trier.de/db/journals/insk/insk35.html#KrauseLHRS12