关键词: 基于信任网络推荐, 用户偏好, 随机游走, 推荐系统, 冷启动

Abstract: In order to improve the accuracy of rating prediction and resolve cold-start problem in recommended systems, on the basis of the TrustWalker model, a random walk model based on user preferences, named PtTrustWalker, was proposed. Firstly, the similarities of users and items were calculated in social networks through matrix factorization method. And then, the items were clustered and the preference of user to items and the user similarity in different categories were calculated through user's scores. Finally, by making use of authority score and user preference, the credibility was detailed into user's credit in different categories, and the score was predicted by the item score of trusted user's highest preference which was similar to the target item in the process of migration. The proposed model decreases the influence of noisy data and improves the stability of the recommendation. The experimental results show that, the PtTrustWalker model has some improvements in the quality and speed of recommendation compared with the existing random walk models.

Key words: trust-based network recommendation, user preference, random walk, recommendation system, cold-start