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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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[1] 张莉, 秦桃, 腾丕强. 一种改进的基于用户聚类的协同过滤算法[J]. 情报科学, 2014, 32(10): 24-27.
[1] (Zhang Li, Qin Tao, Teng Piqiang.An Improved Collaborative Filtering Recmmendatiion Algorithm Based on User Clustering[J]. Information Science, 2014, 32(10): 24-27.)
[2] 方耀宁, 郭云飞, 丁雪涛, 等. 一种基于局部结构的改进奇异值分解推荐算法[J]. 电子与信息学报, 2013, 35(6): 1284-1289.
[2] (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.)
[3] 孙辉, 马跃, 杨海波, 等. 一种相似度改进的用户聚类协同过滤推荐算法[J]. 小型微型计算机系统, 2014, 35(9): 1967-1970.
[3] (Sun Hui, Ma Yue, Yang Haibo, et al.Collaborative Filtering Recommendation Algorithm by Optimizing Similarity and Clustering Users[J]. Journal of Chinese Computer Systems, 2014, 35(9): 1967-1970.)
[4] 高翔. 电子商务个性化推荐系统中协同过滤算法的研究[D]. 南京: 南京航空航天大学, 2011.
[4] (Gao Xiang.Research of Collaborative Filtering on Recommendation Systems for E-Commerce [D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2011.)
[5] 许智宏, 王宝莹. 基于项目综合相似度的协同过滤算法[J]. 计算机应用研究, 2014, 31(2): 398-400.
[5] (Xu Zhihong, Wang Baoying.Collaborative Filtering Recommendation Algorithm Based on Item Complex Similarity[J]. Application Research of Computers, 2014, 31(2): 398-400.)
[6] 文俊浩, 舒珊. 一种改进相似性度量的协同过滤推荐算法[J].计算机科学, 2014, 41(5): 68-71.
[6] (Wen Junhao, Shu Shan.Improves Collaborative Filtering Recommendation Algorithm of Similarity Measure[J]. Computer Science, 2014, 41(5): 68-71.)
[7] 严冬梅, 鲁城华. 基于用户兴趣度和特征的优化协同过滤推荐[J]. 计算机应用研究, 2012, 29(2): 497-500.
[7] (Yan Dongmei, Lu Chenghua.Optimized Collaborative Filtering Recommendation Algorithm Based on Users’ Interest Degree and Feature[J]. Application Research of Computers, 2012, 29(2): 497-500.)
[8] 赵雪. 基于用户兴趣的个性化协同过滤推荐算法研究[D]. 秦皇岛: 燕山大学, 2014.
[8] (Zhao Xue.The Personalized Collaborative Filtering Recommendation Algorithm Based on User Interest [D]. Qinhuangdao: Yanshan University, 2014.)
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