Tag Recommendations in Folksonomies

  • Authors:

  • Robert Jäschke;Leandro Marinho;Andreas Hotho;Lars Schmidt-Thieme;Gerd Stumme

  • Affiliations:

  • Knowledge & Data Engineering Group (KDE), University of Kassel, Wilhelmshöööher Allee 73, 34121 Kassel, Germany and Research Center L3S,Appelstr. 9a, 30167 Hannover, Germany;Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Samelsonplatz 1, 31141 Hildesheim, Germany and Brazilian National Council Scientific and Technological Research (CNP ...;Knowledge & Data Engineering Group (KDE), University of Kassel, Wilhelmshöööher Allee 73, 34121 Kassel, Germany;Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Samelsonplatz 1, 31141 Hildesheim, Germany;Knowledge & Data Engineering Group (KDE), University of Kassel, Wilhelmshöööher Allee 73, 34121 Kassel, Germany and Research Center L3S,Appelstr. 9a, 30167 Hannover, Germany

  • Venue:
  • PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
  • Year:
  • 2007

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Abstract

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.