Recommendation Algorithm Based on Trust Computation and Matrix Factorization
WANG Ruiqin1, PAN Jun2, FENG Jianjun1
1.School of Information Engineering, Huzhou University,Huzhou 313000
2.Institute of Business Modeling and Data Mining, Wenzhou University, Wenzhou 325035
The recommendation algorithm based on matrix factorization has problems of data sparsity, cold start, poor anti-attack ability, etc. Therefore, a trust-based matrix factorization recommendation algorithm is proposed. Firstly, based on the principle of trust generation in social psychology, a reputation-based trust computation method is proposed to alleviate the trust data sparsity problem. Then, grounded on the principle of social homogenization, the user latent factor vector in the process of matrix factorization is extended by using the trust users to solve the rating data sparsity and new-user cold start problem. Meanwhile, social trust relationships are utilized to normalize the target function to improve the accuracy of the rating prediction. Experimental results on Epinions dataset show that the proposed method improves the recommendation precision greatly compared with the state-of-the-art methods, and it effectively solves the problems of data sparsity and cold start.
王瑞琴，潘俊，冯建军. 基于信任计算和矩阵分解的推荐算法[J]. 模式识别与人工智能, 2018, 31(9): 786-796.
WANG Ruiqin, PAN Jun, FENG Jianjun. Recommendation Algorithm Based on Trust Computation and Matrix Factorization. , 2018, 31(9): 786-796.
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2018, Vol. 31 
