A constrained trust recommendation using probabilistic matrix factorization

被引：0

作者：

Yin, Gui-Sheng ^{[1
]}

Zhang, Ya-Nan ^{[1
]}

Dong, Yu-Xin ^{[1
]}

Han, Qi-Long ^{[1
]}

机构：

[1] College of Computer Science and Technology, Harbin Engineering University, Harbin

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2014年 / 42卷 / 05期

关键词：

Constrained trust propagation; Probabilistic matrix factorization; Recommendation algorithm; User cold start problem;

D O I：

10.3969/j.issn.0372-2112.2014.05.011

中图分类号：

学科分类号：

摘要：

Existing recommendation algorithms can not give accurate recommendations for users who have few historical records or even none, namely the user cold recommendation problem. In this paper, a constrained trust recommendation using probabilistic matrix factorization (CTRPMF) is proposed. The trust is propagated with the constraint of distrust to get accurate and comprehensive trust relationship matrix. User trust relationship matrix and user-item matrix are factorized using probabilistic matrix factorization to mix the information from trust relationship and user-item matrix, in order to give recommendations. The experimental results showed that CTRPMF could greatly improve the effectiveness of recommend ations for cold start users and users with sparse historical data, and effectively solve the cold recommendation problem.

引用

页码：904 / 911

页数：7

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