Shilling attack defense algorithm for recommender system based on spectral co-clustering

被引：0

作者：

Yang, Li ^{[1
]}

Niu, Xin-Xin ^{[1
]}

Huang, Wei ^{[2
]}

机构：

[1] School of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing

[2] School of Computer Science, Communication University of China, Beijing

来源：

Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2015年 / 38卷 / 06期

关键词：

Rating deviation from mean agreement; Recommender system; Shilling attack; Spectral co-clustering;

D O I：

10.13190/j.jbupt.2015.06.017

中图分类号：

学科分类号：

摘要：

An algorithm for recommender system based on spectral co-clustering was proposed to defend shilling attacks. The proposed algorithm maintains spectral clustering and co-clustering priors and allows a mixed membership in user and item clusters. The rating deviations were used for mean agreement based on the co-clustering results to recommend for users. Experimental results demonstrated that under the same shilling attack dimensions, our algorithm could decrease the shilling attack affects to recommender systems apparently. © 2015, Beijing University of Posts and Telecommunications. All right reserved.

引用

页码：81 / 86

页数：5

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