[Objective] This paper tries to solve the problems facing traditional collaborative filtering algorithm due to sparse data and few users’ common scores, and then improve the accuracy of the score prediction systems. [Methods] First, we identified users with similar scoring behaviors based on their scoring time. Second, we integrated the similarity of user score variance to the calculation of similarity. [Results] The new algorithm, which reduced the MAE by 2% compared to the traditional algorithm, improved the performance of recommendation system. [Limitations] The proposed algorithm was only examined with the MovieLens dataset, which needed to be expanded to other datasets. [Conclusions] The proposed algorithm can improve the effectiveness of recommendation systems.
李道国,李连杰,申恩平. 基于用户评分时间改进的协同过滤推荐算法*[J]. 现代图书情报技术, 2016, 32(9): 65-69.
Li Daoguo,Li Lianjie,Shen Enping. New Collaborative Filtering Recommendation Algorithm Based on User Rating Time. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2016.09.08.
(Fang Yaoning, Guo Yunfei, Ding Xuetao, et al.An Improved Singular Value Decomposition Recommender Algorithm Based on Local Structures[J]. Journal of Electronics & Information Technology, 2013, 35(6): 1284-1289.)