Shilling Attack Defense Algorithm for Recommender System Based on Spectral Co-Clustering

doi:10.13190/j.jbupt.2015.06.017

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2015, Vol. 38 ›› Issue (6): 81-86.doi: 10.13190/j.jbupt.2015.06.017

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Shilling Attack Defense Algorithm for Recommender System Based on Spectral Co-Clustering

YANG Li¹, NIU Xin-xin¹, HUANG Wei²

1. School of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. School of Computer Science, Communication University of China, Beijing 100024, China

Received:2014-12-14 Online:2015-12-28 Published:2015-12-01

Abstract

Abstract:

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.

Key words: spectral co-clustering, rating deviation from mean agreement, shilling attack, recommender system

CLC Number:

TP301

YANG Li, NIU Xin-xin, HUANG Wei. Shilling Attack Defense Algorithm for Recommender System Based on Spectral Co-Clustering[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2015, 38(6): 81-86.

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Shilling Attack Defense Algorithm for Recommender System Based on Spectral Co-Clustering

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