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HeterRank: addressing information heterogeneity for personalized recommendation in social tagging systems

Published: 16 April 2012 Publication History

Abstract

A social tagging system provides users an effective way to collaboratively annotate and organize items with their own tags. A social tagging system contains heterogenous information like users' tagging behaviors, social networks, tag semantics and item profiles. All the heterogenous information helps alleviate the cold start problem due to data sparsity. In this paper, we model a social tagging system as a multi-type graph and propose a graph-based ranking algorithm called HeterRank for tag recommendation. Experimental results on three publicly available datasets, i.e., CiteULike, Last.fm and Delicious prove the effectiveness of HeterRank for tag recommendation with heterogenous information.

Reference

[1]
R. Jäschke, L. B. Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme. Tag recommendations in folksonomies. In PKDD, 2007.

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  1. HeterRank: addressing information heterogeneity for personalized recommendation in social tagging systems

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        cover image ACM Other conferences
        WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web
        April 2012
        1250 pages
        ISBN:9781450312301
        DOI:10.1145/2187980

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        • Univ. de Lyon: Universite de Lyon

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 16 April 2012

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        1. information heterogeneity
        2. recommender system
        3. social tagging system

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        WWW 2012
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        • Univ. de Lyon
        WWW 2012: 21st World Wide Web Conference 2012
        April 16 - 20, 2012
        Lyon, France

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        Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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