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Folksonomy link prediction based on a tripartite graph for tag recommendation

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Abstract

Nowadays social tagging has become a popular way to annotate, search, navigate and discover online resources, in turn leading to the sheer amount of user-generated metadata. This paper addresses the problem of recommending suitable tags during folksonomy development from a graph-based perspective. The proposed approach adapts the Katz measure, a path-ensemble based proximity measure, for the use in social tagging systems. We model a folksonomy as a weighted, undirected tripartite graph. We then apply the Katz measure to this graph, and exploit it to provide tag recommendations for individual users. We evaluate our method on two real-world folksonomies collected from CiteULike and Last.fm. The experimental results demonstrate that the proposed method improves the recommendation performance and is effective for both active taggers and cold-start taggers compared to existing algorithms.

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Notes

  1. http://citeulike.org

  2. http://www.last.fm/

  3. http://www.citeulike.org/faq/data.adp

  4. http://www.grouplens.org/node/462

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Acknowledgement

This publication was made possible by a grant from the Qatar National Research Fund NPRP 09-052-5-003.

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Authors and Affiliations

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

    Majdi Rawashdeh, Heung-Nam Kim & Abdulmotaleb El Saddik

  2. Department of Computer Science and Engineering, Qatar University, Doha, Qatar

    Jihad Mohamad Alja’am

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  1. Majdi Rawashdeh
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  2. Heung-Nam Kim
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  3. Jihad Mohamad Alja’am
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  4. Abdulmotaleb El Saddik
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Correspondence to Heung-Nam Kim.

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Rawashdeh, M., Kim, HN., Alja’am, J.M. et al. Folksonomy link prediction based on a tripartite graph for tag recommendation. J Intell Inf Syst 40, 307–325 (2013). https://doi.org/10.1007/s10844-012-0227-2

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  • DOI: https://doi.org/10.1007/s10844-012-0227-2

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