The problem with previous studies of social personalized ranking (SPR) algorithms is that they simply integrated the user's social network information into their model, without taking into account the transmission of social trust networks between users. To solve this problem, a new social personalized ranking recommendation algorithm (TrustSPR) based on ListRank algorithm and the newest TrustMF algorithm is proposed, which aims to improve the performance of personalized ranking recommendation algorithm. Experimental results on a real-world dataset showed that the TrustSPR algorithm outperformed state-of-the-art SPR approachs over different evaluation metrics, and that the TrustSPR algorithm possesses good expansibility.