关键词: 推荐系统, 社交网络, 标签系统, 排序学习, 矩阵分解

Abstract: In recent years, recommender system has attracted more and more attention. According to application scenario, recommender system can be divided into rating prediction and Top-K recommendation. Since traditional rating prediction and Top-K recommendation only consider limited dual rating information between users and items, this paper extends a list-wise learning to rank-based matrix factorization method. On one hand, the method fully considers the focusing relationship among users. At first, compute trust values between users based on users’ focusing relationship, then add trust matrix into the original loss function as a social penalty term to make users’ preference vectors as near as possible. On the other hand, the method computes the weights of tags of items, based on which to compute the tag similarities between items, and then add the item tag penalty term to the loss function for training the model. The experimental results on the real Epinions and BaiduMovie datasets show that the proposed method outperforms several traditional methods, especially on the NDCG value, improving the recommendation accuracy effectively.

Key words: recommender system, social networks, tag system, learning to rank, matrix factorization