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Information filtering based on corrected redundancy-eliminating mass diffusion

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  • Xuzhen Zhu
  • Yujie Yang
  • Guilin Chen
  • Matus Medo
  • Hui Tian
  • Shi-Min Cai

Abstract

Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects’ attributes. Based on an unweighted undirected object-user bipartite network, we propose a Corrected Redundancy-Eliminating similarity index (CRE) which is based on a spreading process on the network. Extensive experiments on three benchmark data sets—Movilens, Netflix and Amazon—show that when used in recommendation, the CRE yields significant improvements in terms of recommendation accuracy and diversity. A detailed analysis is presented to unveil the origins of the observed differences between the CRE and mainstream similarity indices.

Suggested Citation

  • Xuzhen Zhu & Yujie Yang & Guilin Chen & Matus Medo & Hui Tian & Shi-Min Cai, 2017. "Information filtering based on corrected redundancy-eliminating mass diffusion," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-15, July.
  • Handle: RePEc:plo:pone00:0181402
    DOI: 10.1371/journal.pone.0181402
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    Cited by:

    1. Xiumei Ma & Yongqiang Sun & Xitong Guo & Kee-hung Lai & Doug Vogel, 2022. "Understanding users’ negative responses to recommendation algorithms in short-video platforms: a perspective based on the Stressor-Strain-Outcome (SSO) framework," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 41-58, March.

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