Abstract: The previous research on social collaborative ranking (SCR) recommendation algorithm only integrates the user's social network information into their model, but does not take into account the transmission of social trust network between users. A problem of the previous research on SCR is data sparsity and cold start that severely degrades quality of recommendation. To solve the problem, a new social collaborative ranking recommendation algorithm (TLRank) based on ListRank (List-wise Learning to Rank) algorithm and the newest TrustMF (Social Collaborative Filtering by Trust) algorithm is proposed to improve the performance of collaborative ranking recommendation by means of elaborately integrating twofold sparse information, the conventional rating data given by users and the social network among the same users. Experimental results on practical datasets show that our proposed TLRank algorithm outperforms the existing CR approaches with reference to the different evaluation metrics and that the algorithm has the advantage of low complexity and is shown to be linear with the number of observed ratings in a given user-item rating matrix. Because of its high precision and good expansibility, TLRank is suitable for processing big data and has a prospect of wide application in the field of internet information recommendation.