Tag Recommendations in Folksonomies

  • Robert Jäschke
  • Leandro Marinho
  • Andreas Hotho
  • Lars Schmidt-Thieme
  • Gerd Stumme
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4702)


Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.

In this paper we evaluate and compare two recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.


Recommender System Collaborative Filter Recommendation Algorithm Graph Base Approach Semiotic Dynamic 
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 2007

Authors and Affiliations

  • Robert Jäschke
    • 1
    • 2
  • Leandro Marinho
    • 3
    • 4
  • Andreas Hotho
    • 1
  • Lars Schmidt-Thieme
    • 3
  • Gerd Stumme
    • 1
    • 2
  1. 1.Knowledge & Data Engineering Group (KDE), University of Kassel, Wilhelmshöher Allee 73, 34121 KasselGermany
  2. 2.Research Center L3S,Appelstr. 9a, 30167 HannoverGermany
  3. 3.Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Samelsonplatz 1, 31141 HildesheimGermany
  4. 4.Brazilian National Council Scientific and Technological Research (CNPq) scholarship holder 

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