The matrix factorization model is one of the classic models in recommendation systems. It can be used to predict users’ ratings on items, and then make recommendations to users to improve user experience. Current matrix factorization models cannot effectively extract the local similarity relationship between users, which leads to poor rating prediction and the cold start problem. With the development of social networks, the trust relationship between users has become an important research tool for recommendation systems. Therefore, this study proposes a local Bayesian probabilistic matrix factorization model based on user trust relationship (TLBPMF) for rating prediction. The model studies users’ ratings by combining the trust relationship information of users. It identifies user groups with similar preferences and clusters them. According to the clustering results, rating submatrixes are obtained. A probabilistic matrix factorization model is established for each submatrix to deeply explore the local similarity relationship between users. The parameters of this model are estimated by the Gibbs sampling algorithm. A rating dataset from a film website is selected for experiments. The results show that the model is superior to the benchmark model in prediction accuracy and has better performance on cold start users.