关键词: 隐式信任, 显式信任, 矩阵分解, 协同过滤

Abstract: Considering the sparsity and cold-start problems of traditional collaborative filtering recommendation algorithms, a new collaborative filtering algorithm based on trust and matrix factorization is presented. Firstly, an implicit trust calculation method based on user’s rating is proposed. This method considers the user’s similarity and interaction experience comprehensively and uses the trust propagation method to make users who do not have direct trust obtain indirect trust. Secondly, the user’s explicit trust information and the implicit trust information are integrated into the SVD++ algorithm through a dynamic factor. Experiments under the FilmTrust dataset show that the proposed algorithm outperforms other matrix decomposition recommendation algorithms on rating prediction and it also performs well on the cold start users’ rating prediction.

Key words: implicit trust, explicit trust, matrix factorization, collaborative filtering