Publikationen

Hayes, C.; Avesani, P. & Veeramachaneni, S. (2007): An Analysis of the Use of Tags in a Blog Recommender System.. In: IJCAI, [Volltext]

2006

Sen, S.; Lam, S. K.; Rashid, A. M.; Cosley, D.; Frankowski, D.; Osterhouse, J.; Harper, F. M. & Riedl, J. (2006): tagging, communities, vocabulary, evolution. In: CSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, New York, NY, USA. [Volltext]

ttarXiv:cs.CY/0605015: Cattuto, C.; Loreto, V. & Pietronero, L. (2006): Collaborative Tagging and Semiotic Dynamics.
[Volltext]

Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G. (2006): Das Entstehen von Semantik in BibSonomy. In: Social Software in der Wertschöpfung, Baden-Baden. [Volltext]

Jäschke, R.; Hotho, A.; Schmitz, C.; Ganter, B. & Stumme, G. (2006): TRIAS - An Algorithm for Mining Iceberg Tri-Lattices. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 06), Hong Kong. [Volltext]

Lambiotte, R. & Ausloos, M. (2006): Collaborative Tagging as a Tripartite Network. In: Computational Science – ICCS 2006,

2007

Koutrika, G.; Effendi, F. A.; Gyöngyi, Z.; Heymann, P. & Garcia-Molina, H. (2007): Combating spam in tagging systems. In: AIRWeb '07: Proc. of the 3rd int. workshop on Adversarial inf. retrieval on the web,

2009

Antonellis, I.; Garcia-Molina, H. & Karim, J. (2009): Tagging with Queries: How and Why?. In: WSDM (Late Breaking-Results), [Volltext]

2007

Grahl, M.; Hotho, A. & Stumme, G. (2007): Conceptual Clustering of Social Bookmarking Sites. In: 7th International Conference on Knowledge Management (I-KNOW '07), Graz, Austria.

2008

Cattuto, C.; Benz, D.; Hotho, A. & Stumme, G. (2008): Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems. In: Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3), Patras, Greece. [Volltext]

2007

Grahl, M.; Hotho, A. & Stumme, G. (2007): Conceptual Clustering of Social Bookmark Sites. In: Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007), [Volltext]

2005

ttarXiv:cs.DS/0512090: Lambiotte, R. & Ausloos, M. (2005): Collaborative tagging as a tripartite network.
[Volltext]

2012

Jäschke, R.; Hotho, A.; Mitzlaff, F. & Stumme, G. (2012): Challenges in Tag Recommendations for Collaborative Tagging Systems. In: Recommender Systems for the Social Web. 32. Aufl./Vol.. Hrsg./Editors: Pazos Arias, J. J.; Fernández Vilas, A. & Díaz Redondo, R. P. Verlag/Publisher: Springer, Berlin/Heidelberg. Erscheinungsjahr/Year: 2012. Seiten/Pages: 65-87. [Volltext]

@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.}
  }

Balby Marinho, L.; Hotho, A.; Jäschke, R.; Nanopoulos, A.; Rendle, S.; Schmidt-Thieme, L.; Stumme, G. & Symeonidis, P. (Hrsg.) (2012): Recommender Systems for Social Tagging Systems. Erscheinungsjahr/Year: 2012. Verlag/Publisher: Springer, [Volltext]

@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.}
  }

2013

Landia, N.; Doerfel, S.; Jäschke, R.; Anand, S. S.; Hotho, A. & Griffiths, N. (2013): Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations. In: cs.IR, Vol. 1310.1498, Erscheinungsjahr/Year: 2013. [Volltext]

Doerfel, S. & Jäschke, R. (2013): An analysis of tag-recommender evaluation procedures. In: Proceedings of the 7th ACM conference on Recommender systems, New York, NY, USA. [Volltext]