Chapter

User Modeling, Adaptation, and Personalization

Volume 7899 of the series Lecture Notes in Computer Science pp 274-280

Predicting Users’ Preference from Tag Relevance

  • Tien T. NguyenAffiliated withGroupLens Research, Computer Science and Engineering, University of Minnesota
  • , John RiedlAffiliated withGroupLens Research, Computer Science and Engineering, University of Minnesota

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Abstract

Tagging has become a powerful means for users to find, organize, understand and express their ideas about online entities. However, tags present great challenges when researchers try to incorporate them into the prediction task of recommender systems. In this paper, we propose a novel approach to infer user preference from tag relevance, an indication of how strong each tag applies to each item in recommender systems. We also present a methodology to choose tags that tell most about each user’s preference. Our preliminary results show that at certain levels, some of our algorithms perform better than previous work.

Keywords

algorithms recommender system mutual information tag relevance