A Constrained Trust Recommendation Using Probabilistic Matrix Factorization
Yin Gui-shen · Dianzi xuebao · 2014
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 w ith 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 show ed that CTRPMF could greatly improve the effectiveness of recommend ations for cold start users and users w ith sparse historical data,and effectively solve the cold recommendation problem.