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A K-medoids Algorithm Based Method to Alleviate the Data Sparsity in Collaborative Filtering
Ziqi Lin1,2; Wancheng Ni1; Haidong Zhang1; Meijing Zhao1; Yiping Yang1
2015-07
Conference Name34th Chinese Control Conference (CCC)
Conference Date2015-7
Conference Place杭州
Abstract

User-based collaborative filtering is an effective and widely-used method in recommender systems. But the data sparsity (the ratings or actions are very sparse for resources) is an inherent limitation of this method. In order to solve the data sparsity, an approach which uses K-medoids algorithm in collaborative filtering is proposed. And the content features of resources are applied to clustering. This approach mainly includes three parts. Firstly, the resources are clustered by K-medoids algorithm. Secondly, the user-behavior data are condensed based on the clustered resources. Thirdly, the recommended list is generated via user-based collaborative algorithm using the compressed user-behavior data. Finally, experiments on data from an Internet education resources sharing platform indicate that the proposed method brings significant improvement both on Recall and Precision in sparse dataset.

KeywordData Sparsity K-medoids Algorithm User-based Collaborative Filtering Recommendation
MOST Discipline Catalogue工学 ; 工学::计算机科学与技术(可授工学、理学学位)
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26221
Collection综合信息系统研究中心
Corresponding AuthorWancheng Ni
Affiliation1.CASIA-HHT Joint Laboratory of Smart Education
2.Integrated Information Research Center, Institute of Automation Chinese Academy of Science
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Ziqi Lin,Wancheng Ni,Haidong Zhang,et al. A K-medoids Algorithm Based Method to Alleviate the Data Sparsity in Collaborative Filtering[C],2015.
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