基于时间相关度和覆盖权重的协同过滤推荐算法

doi:10.16451/j.cnki.issn1003-6059.201904001

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Abstract The traditional item-based collaborative filtering(IBCF) assigns equal weights to all items while computing similarity and prediction. And thus it cannot provide recommendations with high predictive accuracy and classification accuracy. Therefore, a time and covering weighting collaborative filtering(TCWCF) algorithm is proposed. A time-related correlation degree is applied to similarity computation to improve the predictive accuracy, and a covering degree is integrated into rating prediction to increase classification accuracy. Experimental results on MovieLens dataset suggest that TCWCF outperforms traditional IBCF and other algorithms and it provides recommendations with satisfactory predictive accuracy and classification accuracy for users.

Key words： Collaborative Filtering Time-Related Correlation Degree Covering Degree Predictive Accuracy Classification Accuracy

Received: 11 January 2019

ZTFLH:

TP 393

Fund:Support by National Natural Science Foundation of China(No.61772252), Doctoral Start-up Foundation of Liaoning Normal University(No.BS2018L008)

About author:: ZHANG Zhipeng, Ph.D., lecturer. His research interests include data mining and recommender system.ZHANG Yao, Ph.D., lecturer. Her research interests include pattern recognition and simulation analysis.REN Yonggong(Corresponding author), Ph.D., professor. His research interests include artificial intelligence and data mining.

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	ZHANG Zhipeng
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Cite this article:

ZHANG Zhipeng,ZHANG Yao,REN Yonggong. Collaborative Filtering Recommendation Algorithm Based on Time-Related Correlation Degree and Covering Degree[J]. , 2019, 32(4): 289-297.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201904001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I4/289

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