Focused on the issue that the most existing social recommendation algorithms ignore the influence of the association relationship between items on the recommendation accuracy， and fail to effectively combine user ratings with trust data， a Social recommendation algorithm combing Trust implicit similarity and Score similarity （SocialTS） was proposed. Firstly， the score similarity and trust implicit similarity between users were combined linearly to obtain reliable similar friends among users. Then， the trust relationship was integrated into the correlation analysis of items， and the modified similar items were obtained. Finally， similar users and items were added to the Matrix Factorization （MF） model as regularization terms， thereby obtaining more accurate feature representations of users and items. Experimental results show that on FilmTrust and CiaoDVD datasets， when the latent feature dimension is 10， compared with the mainstream social recommendation algorithm Trust-based Singular Value Decomposition （TrustSVD）， SocialTS has the Root Mean Square Error （RMSE） reduced by 4.23% and 8.38% respectively， and the Mean Absolute Error （MAE） reduced by 4.66% and 6.88% respectively. SocialTS can not only effectively improve users' cold start problem， but also accurately predict users' actual ratings under different numbers of ratings， and has good robustness.