期刊名称：International Journal of Grid and Distributed Computing
印刷版ISSN：2005-4262
出版年度：2016
卷号：9
期号：12
页码：203-212
出版社：SERSC
摘要：Traditional methods UBCF have limitations of poor recommendation quality and problems of data sparsity. To alleviate these problems, a novel collaborative filtering algorithm is designed, which firstly get the users’ ratings and time intervals for each attribute from the users’ ratings for items, then produce two methods to calculate the similarity between users, introduce a weighting parameters to control the weight between the two similarity methods in order to get a fusion similarity between two users. The results show that this method is able to improve the accuracy of predicted values, resulting in improving recommendation quality of the collaborative filtering recommendation algorithm.
关键词：attribute ratings; time interval; similarity; fusion
