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A graph-based meta-approach for tag recommendation

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Complex Networks & Their Applications V (COMPLEX NETWORKS 2016 2016)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 693))

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Abstract

In this paper we propose a graph-coarsening approach that aims to speed-up the execution time of graph-based tag recommenders in large-scale folksonomies. A community detection algorithm in multiplex networks is applied for coarsening the hypergraph depicting a folksonomy. Experiments on real datasets show the validity of the approach.

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Author information

Authors and Affiliations

  1. LIPN UMR CNRS 7030, SPC-UP13, Paris, France

    Manel Hmimida & Rushed Kanawati

Authors
  1. Manel Hmimida
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  2. Rushed Kanawati
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Corresponding authors

Correspondence to Manel Hmimida or Rushed Kanawati .

Editor information

Editors and Affiliations

  1. University of Burgundy , Dijon, France

    Hocine Cherifi

  2. Computer Science Department, University of Milan Computer Science Department, Milan, Italy

    Sabrina Gaito

  3. IMT Lucca , Lucca, Italy

    Walter Quattrociocchi

  4. Blanchardstown Business and Tech Park, Bell Labs-Nokia Blanchardstown Business and Tech Park, Blanchardstown, Ireland

    Alessandra Sala

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Hmimida, M., Kanawati, R. (2017). A graph-based meta-approach for tag recommendation. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_25

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  • DOI: https://doi.org/10.1007/978-3-319-50901-3_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50900-6

  • Online ISBN: 978-3-319-50901-3

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