In view of the fact that users' preferences and social interaction data are very sparse, and the fact that users may prefer products recommended by friends than recommended by foes, a regularized matrix decomposition recommendation algorithm integrating with social network and interest preference similarity was proposed. First of all, for the problem of sparse data of social relations. Global and local topological characteristics of the network were used to extract trust and distrust matrices between users respectively. Secondly, a method for calculating interest preference similarity between users was defined. Finally, in the process of matrix decomposition, the trust matrix, the distrust matrix, and the interest correlation were synthetically taken into consideration to make recommendations for the users. Experiments show that this method is superior to other regularization recommendation methods. Compared with the basic matrix decomposition model (SocialMF), SoRec, TrustMF, CTRPMF and RecSSN algorithm, the proposed algorithm reduces 1.1% to 9.5% and 2% to 10.1% respectively in the root mean square error (RMSE) and the mean absolute error (MAE), improved recommendations effectively.