Personalized Collaborative Filtering Recommendation Approach Based on Covering Reduction

被引：0

作者：

Zhang Z. ^{[1
]}

Zhang Y. ^{[2
]}

Ren Y. ^{[1
]}

机构：

[1] School of Computer and Information Technology, Liaoning Normal University, Dalian

[2] School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Collaborative Filtering; Covering Reduction; Personalized Recommendation; Recommender System;

D O I：

10.16451/j.cnki.issn1003-6059.201907004

中图分类号：

学科分类号：

摘要：

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. © 2019, Science Press. All right reserved.

引用

页码：607 / 614

页数：7

共 18 条

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