Applying latent semantic analysis to tag-based community recommendations

Authors:
Aysha Akther;Heung-Nam Kim;Majdi Rawashdeh;Abdulmotaleb El Saddik
Affiliations:
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, Canada;School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, Canada;School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, Canada;School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, Canada
Venue:
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Year:
2012

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Abstract

With the explosive growth of social communities, users of social Web systems have experienced considerable difficulty with discovering communities relevant to their interests. In this paper we address the problem of recommending communities (or groups) to individual users. We regard this problem as tag-based personalized searches. Based on social tags used by members of communities, we first represent communities in a low-dimensional space, the so-called latent semantic space, by using Latent Semantic Analysis. Then, for recommending communities to a given user, we capture how each community is relevant to both that user's personal tag usage and other community members' tagging patterns in the latent space. Our evaluation on the CiteULike dataset shows that our approach can significantly improve the recommendation quality.