The problem with previous studies of social One Class Collaborative Ranking (OCCR) 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 one class collaborative ranking recommendation algorithm (TrustOCCR) based on CLiMF model and the newest TrustMF model is proposed, which aims to improve the performance of social one class collaborative ranking recommendation by integrating twofold sparse data, i.e., implicit feedback data and the transitive social trust network data. Experimental results on practical dataset show that our proposed TrustOCCR algorithm outperformed existing state-of-the-art OCCF approach over di?erent evaluation metrics, and that the TrustOCCR algorithm possesses good expansibility and is suitable for processing big data in the ?eld of internet information recommendation.