With the increase of online commodity transaction volume and the further development of social network, recommendation system has become one of the 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 the user starts cold. A novel pairwise learning to socialized ranking recommendation algorithm based on implicit feedback information is proposed in this article. The algorithm first calculates user preferences among different items using a matrix factorization method. Secondly, user preference information is incorporated into the Bayesian Personalized Ranking (BPR) algorithm. Similar relationships between users as well as direct and indirect relationships between trusting users are then mined and quantified in order to study differences in user preferences across projects. Finally, these trust relationships are 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 is verified on the DouBan dataset and FilmTrust dataset. The ranking evaluation metrics of Precision, MAP, and NGCD are used to verify the merits of ranking recommendation between the proposed algorithm in this paper and SBPR, TBPR, BPRMF, and MostPopular algorithms. Ranking recommendation tests are performed on these two specific social datasets for the full dataset and user cold start, respectively. The experimental results demonstrate that the proposed algorithm in this paper significantly outperforms other ranking recommendation algorithms and can achieve better recommendation accuracy.It can be seen that the algorithm can improve the problem of poor recommendation due to data sparsity and user cold start.
