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Journal of Intelligent Information Systems

, Volume 46, Issue 2, pp 349–367 | Cite as

Robust recommendation method based on suspicious users measurement and multidimensional trust

  • Huawei Yi
  • Fuzhi ZhangEmail author
Article

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.

Keywords

Shilling attacks Robust recommendation Multidimensional trust Matrix factorization Relevance vector machine Analytic hierarchy process 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.School of Information Science and EngineeringYanshan UniversityQinhuangdaoChina
  2. 2.The Key Laboratory for Computer Virtual Technology and System Integration of Hebei ProvinceQinhuangdaoChina

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