A Comparison of Content-Based Tag Recommendations in Folksonomy Systems

  • Jens Illig
  • Andreas Hotho
  • Robert Jäschke
  • Gerd Stumme
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6581)


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.


Recommender System Recommendation Algorithm Cold Start Problem Social Bookmark Content Base Recommender 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jens Illig
    • 1
  • Andreas Hotho
    • 1
  • Robert Jäschke
    • 1
    • 2
  • Gerd Stumme
    • 1
    • 2
  1. 1.Knowledge & Data Engineering Group, Department of Mathematics and Computer ScienceUniversity of KasselKasselGermany
  2. 2.Research Center L3SHannoverGermany

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