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Balancing the popularity bias of object similarities for personalised recommendation

Author

Listed:
  • Lei Hou

    (Informatics Research Centre, Henley Business School, University of Reading, Reading)

  • Xue Pan

    (Informatics Research Centre, Henley Business School, University of Reading, Reading)

  • Kecheng Liu

    (Informatics Research Centre, Henley Business School, University of Reading, Reading
    Data Science and Cloud Service Research Centre, Shanghai University of Finance and Economics)

Abstract

Network-based similarity measures have found wide applications in recommendation algorithms and made significant contributions for uncovering users’ potential interests. However, existing measures are generally biased in terms of popularity, that the popular objects tend to have more common neighbours with others and thus are considered more similar to others. Such popularity bias of similarity quantification will result in the biased recommendations, with either poor accuracy or poor diversity. Based on the bipartite network modelling of the user-object interactions, this paper firstly calculates the expected number of common neighbours of two objects with given popularities in random networks. A Balanced Common Neighbour similarity index is accordingly developed by removing the random-driven common neighbours, estimated as the expected number, from the total number. Recommendation experiments in three data sets show that balancing the popularity bias in a certain degree can significantly improve the recommendations’ accuracy and diversity simultaneously.

Suggested Citation

  • Lei Hou & Xue Pan & Kecheng Liu, 2018. "Balancing the popularity bias of object similarities for personalised recommendation," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 91(3), pages 1-7, March.
    • Handle: RePEc:spr:eurphb:v:91:y:2018:i:3:d:10.1140_epjb_e2018-80374-8
    DOI: 10.1140/epjb/e2018-80374-8
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    Cited by:

    1. João Pedro Baptista & Anabela Gradim, 2020. "Understanding Fake News Consumption: A Review," Social Sciences, MDPI, vol. 9(10), pages 1-22, October.
    2. Gaetano Lisi, 2023. "Online disinformation: an economic analysis," Economics and Business Letters, Oviedo University Press, vol. 12(4), pages 266-276.

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    Keywords

    Statistical and Nonlinear Physics;

    Statistics

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