GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Recency-based collaborative filtering
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
TDCF: Time Distribution Collaborative Filtering Algorithm
ISISE '08 Proceedings of the 2008 International Symposium on Information Science and Engieering - Volume 01
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Collaborative filtering (CF) algorithms predict interests of an active user in order to deal with the overload of information. Usually, changes of her interests have been ignored in traditional algorithms, which take user's interest as static data and product rating in different phase with same weight. So when users' interests have changed as time goes on, unneeded items may be recommended. In order to solve above problem, we propose a new item-based collaborative filtering algorithm in this paper. In this algorithm, named PFCF, we firstly divide users' rating history into several periods, then users' interests distributing in these periods are analyzed by a phrased forecast method, which is used to find user's different type interests. The proposed algorithm is strictly tested on the MovieLens data set. The experimental results show its good precision against other traditional item-based collaborative filtering algorithms.