关键词: 社会网络, 权威, 可信度, 矩阵分解, 推荐

Abstract: Concerning data sparsity and malicious behavior of traditional collaborative filtering algorithm, a new social recommendation method combining trust and matrix factorization was proposed in this paper. Firstly, the incredible nodes in the network were founded by computing their prestige value and bias value, and then the weight of their evaluation would be weakened. Finally, the collaborative recommendation was conducted under the social network circumstance by combining the user-item matrix and trust matrix. The experimental results show that the proposed algorithm reduces the importance of not credible node to weaken the negative influence the false or malicious score brings to recommendation system, the data sparsity and malicious behavior problems can be alleviated, and a higher prediction accuracy than that of the traditional collaborative filtering algorithms can be achieved.

Key words: social network, authority, credibility, matrix factorization, recommendation