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  2019, Vol. 32 Issue (7): 607-614    DOI: 10.16451/j.cnki.issn1003-6059.201907004
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Personalized Collaborative Filtering Recommendation Approach Based on Covering Reduction
ZHANG Zhipeng1, ZHANG Yao2, REN Yonggong1
1.School of Computer and Information Technology, Liaoning Normal University, Dalian 116029
2.School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116034

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Abstract  

Collaborative filtering(CF) cannot provide personalized recommendation with both good accuracy and diversity. To address this problem, a covering reduction collaborative filtering(CRCF) is proposed in this paper. The covering reduction algorithm in covering based rough sets is combined with user reduction in CF, and redundant elements of covering are matched with redundant users of a neighbor. The redundant users are removed by covering reduction algorithm to ensure high effectiveness of the neighbor of a target user in CF. Experimental results on public datasets indicate that CRCF provides personalized recommendations for target users with both satisfactory accuracy and diversity in sparse data environment.

Key wordsRecommender System      Collaborative Filtering      Covering Reduction      Personalized Recommendation     
Received: 20 March 2019     
ZTFLH: TP 393  
Fund:

Support by National Natural Science Foundation of China(No.61772252), Ph.D. 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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Cite this article:   
ZHANG Zhipeng,ZHANG Yao,REN Yonggong. Personalized Collaborative Filtering Recommendation Approach Based on Covering Reduction[J]. , 2019, 32(7): 607-614.
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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201907004      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I7/607
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