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Exploring social tagging for personalized community recommendations

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Abstract

Users of social Web sites actively create and join communities as a way to collectively share their media content and rich experience with diverse groups of people. In this study we focus on the issue of recommending social communities (or groups) to individual users. We address specifically the potential of social tagging for accentuating users’ interests and characterizing communities. We also discuss some unique methods of improving several techniques that have been adapted for use in the context of community recommendations: collaborative filtering, a random walk model, a Katz influence model, a latent semantic model, and a user-centric tag model. We effectively incorporate social tagging information in each algorithm. We present empirical evaluations using real datasets from CiteULike and Last.fm. Our experimental results demonstrate that the different algorithms incorporated with social tagging offer significant advantages in improving both the recommendation quality and coverage, and demonstrate their feasibility for community recommendations in dealing with sparsity-related limitations.

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  1. School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward, Ottawa, ON, K1N 6N5, Canada

    Heung-Nam Kim & Abdulmotaleb El Saddik

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  1. Heung-Nam Kim
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  2. Abdulmotaleb El Saddik
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Correspondence to Heung-Nam Kim.

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Kim, HN., El Saddik, A. Exploring social tagging for personalized community recommendations. User Model User-Adap Inter 23, 249–285 (2013). https://doi.org/10.1007/s11257-012-9130-3

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