基于信任计算和矩阵分解的推荐算法

doi:10.16451/j.cnki.issn1003-6059.201809002

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Abstract

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.

Key words： Social Trust Reputation Trust Propagation Matrix Factorization Regularization

Received: 22 May 2018

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	Articles by authors
	WANG Ruiqin
	PAN Jun
	FENG Jianjun

Cite this article:

WANG Ruiqin,PAN Jun,FENG Jianjun. Recommendation Algorithm Based on Trust Computation and Matrix Factorization[J]. , 2018, 31(9): 786-796.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201809002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I9/786

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