所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：Lect. Notes Comput. Sci.
摘要：The traditional collaborative filtering algorithms are more successful used for personalized recommendation. However, the traditional collaborative filtering algorithm usually has issues such as low recommendation accuracy and cold start. Aiming at addressing the above problems, a hybrid collaborative filtering algorithm using double neighbor selection is proposed. Firstly, according to the results of user’s dynamic similarity calculation, the similar interest sets of the target users may be dynamically selected. Analyzing the dynamic similar interest set of the target user, we can divide the users into two categories, one is an active user, and the other is a non-active user. For the active user, by calculating the trust degree of the users with similar interests, we can select the user with the trust degree of TOP-N, and recommend the target user. For the non-active user, the neighbor user may be found according to the similarity of the user on some attributes, and them with high similarity will be recommend to the target user. The experimental results show that the algorithm not only improves the recommending accuracy of the recommendation system, but also effectively solves the problem of data sparseness and user cold start. © 2019, Springer Nature Switzerland AG.
ISSN号：0302-9743
是否译文：否
发表时间：2019-01-01
合写作者：Qin, Xiaofan,汪清
通讯作者：谭文安
发表时间：2019-01-01