Chapter

Knowledge Discovery in Databases: PKDD 2007

Volume 4702 of the series Lecture Notes in Computer Science pp 506-514

Tag Recommendations in Folksonomies

  • Robert JäschkeAffiliated withKnowledge & Data Engineering Group (KDE), University of Kassel, Wilhelmshöher Allee 73, 34121 KasselResearch Center L3S,Appelstr. 9a, 30167 Hannover
  • , Leandro MarinhoAffiliated withInformation Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Samelsonplatz 1, 31141 HildesheimBrazilian National Council Scientific and Technological Research (CNPq) scholarship holder
  • , Andreas HothoAffiliated withKnowledge & Data Engineering Group (KDE), University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel
  • , Lars Schmidt-ThiemeAffiliated withInformation Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Samelsonplatz 1, 31141 Hildesheim
  • , Gerd StummeAffiliated withKnowledge & Data Engineering Group (KDE), University of Kassel, Wilhelmshöher Allee 73, 34121 KasselResearch Center L3S,Appelstr. 9a, 30167 Hannover

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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.