关键词: 用户偏好, 非对称, 协同过滤, 推荐算法

Abstract: To solve the problem of not considering the ratio of co-rated items and the user preference in some traditional similarity measures, two weighting factors, asymmetry factor and preference factor, are added to improve the accuracy of similarity computation. The experimental results on two public datasets, Movielens and Yahoo Music, show that the proposed model significantly lowers the prediction error and has better performance than other similarities. The model with these two factors can better reflect the rating difference between users, and effectively deal with the user preference effectively. Thus, it improves the recommendation quality.

Key words: user preference, asymmetry, collaborative filtering, recommendation algorithm