International Conference on User Modeling, Adaptation, and Personalization

UMAP 2013: User Modeling, Adaptation, and Personalization pp 274-280

Predicting Users’ Preference from Tag Relevance

  • Tien T. Nguyen
  • John Riedl
Conference paper

DOI: 10.1007/978-3-642-38844-6_23

Volume 7899 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Nguyen T.T., Riedl J. (2013) Predicting Users’ Preference from Tag Relevance. In: Carberry S., Weibelzahl S., Micarelli A., Semeraro G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg

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 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tien T. Nguyen
    • 1
  • John Riedl
    • 1
  1. 1.GroupLens Research, Computer Science and EngineeringUniversity of MinnesotaMinneapolisUSA