A constrained trust recommendation using probabilistic matrix factorization

作者：Yin, Gui Sheng; Zhang, Ya Nan^*; Dong, Yu Xin; Han, Qi Long

来源：Acta Electronica Sinica, 2014, 42(5): 904-911.

DOI：10.3969/j.issn.0372-2112.2014.05.011

• Cold start problems
• Historical data
• Historical records
• Probabilistic matrix factorizations
• Recommendation algorithms
• Trust propagation
• Trust relationship
• User-item matrix

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.

• 出版日期2014
• 单位哈尔滨工程大学