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.
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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. https://doi.org/10.1007/978-3-642-38844-6_23
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DOI: https://doi.org/10.1007/978-3-642-38844-6_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38843-9
Online ISBN: 978-3-642-38844-6
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