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Shilling attack detection for recommender systems based on credibility of group users and rating time series

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  • Wei Zhou
  • Junhao Wen
  • Qiang Qu
  • Jun Zeng
  • Tian Cheng

Abstract

Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user’s credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method.

Suggested Citation

  • Wei Zhou & Junhao Wen & Qiang Qu & Jun Zeng & Tian Cheng, 2018. "Shilling attack detection for recommender systems based on credibility of group users and rating time series," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-17, May.
  • Handle: RePEc:plo:pone00:0196533
    DOI: 10.1371/journal.pone.0196533
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    References listed on IDEAS

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    1. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
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    1. Chengjun Zhang & Jin Liu & Yanzhen Qu & Tianqi Han & Xujun Ge & An Zeng, 2018. "Enhancing the robustness of recommender systems against spammers," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-14, November.

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