关键词: 协同谱聚类, 分级偏离平均度, 托攻击, 推荐系统

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