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Robust recommendation method based on suspicious users measurement and multidimensional trust

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Abstract

The existing collaborative recommendation algorithms have poor robustness against shilling attacks. To address this problem, in this paper we propose a robust recommendation method based on suspicious users measurement and multidimensional trust. Firstly, we establish the relevance vector machine classifier according to the user profile features to identify and measure the suspicious users in the user rating database. Secondly, we mine the implicit trust relation among users based on the user-item rating data, and construct a reliable multidimensional trust model by integrating the user suspicion information. Finally, we combine the reliable multidimensional trust model, the neighbor model and matrix factorization model to devise a robust recommendation algorithm. The experimental results on the MovieLens dataset show that the proposed method outperforms the existing methods in terms of both recommendation accuracy and robustness.

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Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China

    Huawei Yi & Fuzhi Zhang

  2. The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, 066004, China

    Huawei Yi & Fuzhi Zhang

Authors
  1. Huawei Yi
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  2. Fuzhi Zhang
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Corresponding author

Correspondence to Fuzhi Zhang.

Additional information

This work was supported by the National Natural Science Foundation of China (No.61379116), the Natural Science Foundation of Hebei Province, China (No. F2013203124, No. F2015203046) , the Key Program of Research on Science and Technology of Higher Education Institutions of Hebei Province, China (No. ZH2012028) and the Scientific Research Foundation of Liaoning Provincial Education Department, China (No. L2015240).

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Yi, H., Zhang, F. Robust recommendation method based on suspicious users measurement and multidimensional trust. J Intell Inf Syst 46, 349–367 (2016). https://doi.org/10.1007/s10844-015-0375-2

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  • DOI: https://doi.org/10.1007/s10844-015-0375-2

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