Traditional collaborative filtering algorithms suffer from data sparsity and cold start problems. Taking advantage of rich information in social networks brings an opportunity to alleviate the problems of data sparsity and cold start. However, the traditional social network-based collaborative filtering algorithm only use the coarse-grained and sparse trust relationships to improve recommendation quality, i.e. they only utilize 0 or 1 to denote the trust relationships between users. In addition, the traditional social network based recommendation algorithms only integrate explicit trust relationships, and ignore implicit trust relationships. In this paper, we propose a graph embedding model based collaborative filtering algorithm. Specifically, we adopt the graph embedding technique to learn the low-dimensional embedded representations of users in social networks, and infer the fine-grained trust relationship between users based on the low-dimensional embedded representations. Finally, the user’s rating of the target item is predicted based on the scoring weights of the target item by the trusted user and the similar one. Experimental results on the actual data sets prove that the performance of the collaborative filtering algorithm based on the graph embedding model is better than that of the traditional collaborative filtering algorithms.