Collaborative filtering recommendation algorithm has been widely used because it is not limited by the knowledge in a specific domain and easy to implement. However, it is faced with the problem of several issues such as data sparsity, extensibility and cold start which affect the effectiveness of the recommendation algorithm in some practical application scenarios. To address the user cold start problem, by merging social trust information (i.e., trusted neighbors explicitly specified by users) and rating information, a collaborative filtering recommendation algorithm based on information of community experts is proposed in this paper. First of all, users are divided into different communities based on their social relations. Then, experts in each community are identified according to some criteria. In addition, in order to alleviate the impact of the data sparsity, ratings of an expert’s trusted neighbors are merged to complement the ratings of the expert. Finally, the prediction for a given item is generated by aggregating the ratings of experts in the community of the target user. Experimental results based on two real-world data sets FilmTrust and Epinions show the proposed algorithm is able to alleviate the user cold start problem and superior to other algorithms in terms of MAE and RMSE.