Probabilistic Matrix Factorization Recommendation Algorithm with User Trust Similarity

被引：3
|
作者：
Dong, Yuxin [1 ]
Fang, Shuyun [1 ]
Jiang, Kai [1 ]
Chen, Fukun [1 ]
Yin, Guisheng [1 ]
机构：
[1] Harbin Engn Univ, Coll Comp Sci & Technol, Harbin, Peoples R China
来源：
2018 3RD INTERNATIONAL CONFERENCE ON MEASUREMENT INSTRUMENTATION AND ELECTRONICS (ICMIE 2018) | 2018年 / 208卷
关键词：
D O I：
10.1051/matecconf/201820805004
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we describe the formatting guidelines for Conference Proceedings. Whether the user similarity calculation is reasonable in the traditional collaborative filtering recommendation algorithm directly affects the result of the collaborative filtering recommendation algorithm. This paper proposes a probabilistic matrix factorization recommendation algorithm with user trust similarity which combines improved similarity of users' trust and probability matrix factorization recommendation method. The results show that proposed algorithm could relieve user cold start issues and effectively reduce the error of recommendation.
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收藏
页数：5
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