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New Technology of Library and Information Service  2016, Vol. 32 Issue (9): 65-69    DOI: 10.11925/infotech.1003-3513.2016.09.08
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New Collaborative Filtering Recommendation Algorithm Based on User Rating Time
Li Daoguo1,Li Lianjie2(),Shen Enping2
1School of Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
2School of Management, Hangzhou Dianzi University, Hangzhou 310018, China
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[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.

Key wordsCollaborative filtering      Data sparsity      Similarity score      User rating variance      similarity Nearest neighbor     
Received: 22 April 2016      Published: 19 October 2016

Cite this article:

Li Daoguo,Li Lianjie,Shen Enping. New Collaborative Filtering Recommendation Algorithm Based on User Rating Time. New Technology of Library and Information Service, 2016, 32(9): 65-69.

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