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Journal of Intelligent Information Systems

Erschienen in:

01.04.2016

Robust recommendation method based on suspicious users measurement and multidimensional trust

verfasst von: Huawei Yi, Fuzhi Zhang

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 2/2016

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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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Titel: Robust recommendation method based on suspicious users measurement and multidimensional trust
verfasst von: Huawei Yi
Fuzhi Zhang
Publikationsdatum: 01.04.2016
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 2/2016
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-015-0375-2

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