The traditional social collaborative filtering algorithms based on rating prediction have the inherent deficiency in which the prediction value does not match the real sort， and social collaborative ranking algorithms based on ranking prediction are more suitable to practical application scenarios. However， most existing social collaborative ranking algorithms focus on explicit feedback data only or implicit feedback data only， and not make full use of the information in the dataset. In order to fully exploit both the explicit and implicit scoring information of users’ social networks and recommendation objects， and to overcome the inherent deficiency of traditional social collaborative filtering algorithms based on rating prediction， a new social collaborative ranking model based on the newest xCLiMF model and TrustSVD model， namely SPR_SVD++， was proposed. In the algorithm， both the explicit and implicit information of user scoring matrix and social network matrix were exploited simultaneously and the learning to rank’s evaluation metric Expected Reciprocal Rank （ERR） was optimized. Experimental results on real datasets show that SPR_SVD++ algorithm outperforms the existing state-of-the-art algorithms TrustSVD， MERR_SVD++ and SVD++ over two different evaluation metrics Normalized Discounted Cumulative Gain （NDCG） and ERR. Due to its good performance and high expansibility， SPR_SVD++ algorithm has a good application prospect in the Internet information recommendation field.