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
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.
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2019, Vol. 32 
