关键词: HITS, 信任关系, 偏好兴趣, 协同过滤, 推荐算法

Abstract: The product recommendation effect was affected by the trust relationship among users， the preference interest for goods and the time factor. These factors were introduced to the basic HITS algorithm， and the HITS algorithm was improved. The user preference interest matrix was also improved， which uses implicit data to estimate users′ preference for goods using logistic regression algorithm. The situation with zero score gives different preference values， which is more consistent with reality. A hybrid recommendation model was proposed by combining the improved HITS algorithm with the collaborative filtering algorithm， and users were divided into active users and inactive users for recommendation. The proposed algorithm was tested using the Movielens data set， the results showed that the algorithm could generate better recommendation result in sparse data sets and cold-start situation， and it outperforms user-based collaborative filtering algorithm.

Key words: HITS(hypertext induced topic search), trust relationship, preference interest, collaborative filtering, recommendation algorithm