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

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.