Leveraging collaborative filtering to tag-based personalized search

Authors:
Heung-Nam Kim;Majdi Rawashdeh;Abdulmotaleb El Saddik
Affiliations:
School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada;School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada;School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada
Venue:
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Year:
2011

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Abstract

In recent years, social media services with social tagging have become tremendously popular. Because users are no longer mere consumers of content, social Web users have been overwhelmed by the huge numbers of social content available. For tailoring search results, in this paper, we look into the potential of social tagging in social media services. By leveraging collaborative filtering, we propose a new search model to enhance not only retrieval accuracy but also retrieval coverage. Our approach first computes latent preferences of users on tags from other similar users, as well as latent annotations of tags for items from other similar items. We then apply the latency of tags to a tag-based personalized ranking depending on individual users. Experimental results demonstrate the feasibility of our method for personalized searches in social media services.