Predicting Users’ Preference from Tag Relevance
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.
Keywordsalgorithms recommender system mutual information tag relevance
Unable to display preview. Download preview PDF.
- 1.Bell, R., Koren, Y.: Lessons from the Netflix prize challenge. ACM SIGKDD Explorations Newsletter Special issue on Visual Analytics, 75–79 (2007)Google Scholar
- 2.Chang, C., Lin, C.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3), 27:1–27:27 (2011)Google Scholar
- 3.Sen, S., Vig, J., Riedl, J.: Tagommenders: connecting users to items through tags. In: ACM International Conference on World Wide Web, WWW, pp. 221–230 (2009)Google Scholar
- 4.Sen, S., Vig, J., Riedl, J.: Learning to recognize valuable tags. In: Proceedings of the 14th International Conference on Intelligent user Interfaces, IUI, pp. 87–96 (2009)Google Scholar
- 5.Shirky, C.: Ontology is overrated (2005), http://www.shirky.com/writings/ontology_overrated.html (retrieved on December 28, 2012)
- 7.Vig, J., Sen, S., Riedl, J.: Encoding Community Knowledge to Support Novel Interaction. ACM Transactions on Interactive Intelligent Systems, TiiS 2(3) (2012)Google Scholar