Abstract:With the increase of online commodity transaction volume and the further development of social network, recommendation system has become one of important tools to solve information overload. However, when the data sparsity of the score matrix is relatively large, the recommendation accuracy will decrease significantly, especially when the user starts cold. A new social ranking recommendation algorithm based on implicit feedback information was proposed in this paper. The user preferences among different items using a matrix factorization method were first calculated by the proposed algorithm. Secondly, the user preference information was incorporated into the Bayesian Personalized Ranking (BPR) algorithm. Then, the similar relationships between users as well as direct and indirect relationships between trusting users were mined and quantified in order to study the differences of user preferences across projects. Finally, these trust relationships were fused with the BPR algorithm to build a social ranking recommendation model. To validate the proposed social ranking recommendation algorithm, the validity of the algorithm was verified on the DouBan dataset and FilmTrust dataset. The ranking evaluation metrics of Precision, MAP, and NGCD were used to verify the merits of ranking recommendation between the proposed algorithm and SBPR, TBPR, BPR, and MostPopular algorithms. The ranking recommendation tests were performed on these two specific social datasets for the full dataset and user cold start, respectively. The experimental results demonstrated that the proposed algorithm significantly outperforms other ranking recommendation algorithms and achieves better recommendation accuracy. It can be seen that the algorithm can improve the poor recommendation performance caused by data sparsity and user cold start.