IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0181402.html
   My bibliography  Save this article

Information filtering based on corrected redundancy-eliminating mass diffusion

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0181402
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0181402&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0181402?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0181402. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.