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A New Recommender System Using Context Clustering Based on Matrix Factorization Techniques

2016; Institution of Engineering and Technology; Volume: 25; Issue: 2 Linguagem: Inglês

10.1049/cje.2016.03.021

2075-5597

Xiaoyao Zheng, Yonglong Luo, Liping Sun, Fulong Chen,

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Chinese Journal of ElectronicsVolume 25, Issue 2 p. 334-340 ArticleFree Access A New Recommender System Using Context Clustering Based on Matrix Factorization Techniques Xiaoyao Zheng, Xiaoyao Zheng College of Territorial Resources and Tourism, Anhui Normal University, Wuhu, 241003 China School of Mathematics and Computer Science, Anhui Normal University, Wuhu, 241003 ChinaSearch for more papers by this authorYonglong Luo, Corresponding Author Yonglong Luo ylluo@ustc.edu.cn School of Mathematics and Computer Science, Anhui Normal University, Wuhu, 241003 ChinaSearch for more papers by this authorLiping Sun, Liping Sun College of Territorial Resources and Tourism, Anhui Normal University, Wuhu, 241003 China School of Mathematics and Computer Science, Anhui Normal University, Wuhu, 241003 ChinaSearch for more papers by this authorFulong Chen, Fulong Chen School of Mathematics and Computer Science, Anhui Normal University, Wuhu, 241003 ChinaSearch for more papers by this author Xiaoyao Zheng, Xiaoyao Zheng College of Territorial Resources and Tourism, Anhui Normal University, Wuhu, 241003 China School of Mathematics and Computer Science, Anhui Normal University, Wuhu, 241003 ChinaSearch for more papers by this authorYonglong Luo, Corresponding Author Yonglong Luo ylluo@ustc.edu.cn School of Mathematics and Computer Science, Anhui Normal University, Wuhu, 241003 ChinaSearch for more papers by this authorLiping Sun, Liping Sun College of Territorial Resources and Tourism, Anhui Normal University, Wuhu, 241003 China School of Mathematics and Computer Science, Anhui Normal University, Wuhu, 241003 ChinaSearch for more papers by this authorFulong Chen, Fulong Chen School of Mathematics and Computer Science, Anhui Normal University, Wuhu, 241003 ChinaSearch for more papers by this author First published: 01 March 2016 https://doi.org/10.1049/cje.2016.03.021Citations: 12AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Recommender system can efficiently alleviate the information overload problem, but it has been trapped in the recommendation accuracy. We proposed a new recommender system which based on matrix factorization techniques. More factors including contextual information, user ratings and item feature are all taken into consideration. Meanwhile the k-modes algorithm is used to reduce the complexity of matrix operations and increase the relevance of the user-item ratings sub-matrix. Compared with several major existing recommendation approaches, extensive experimental evaluation on publicly available dataset demonstrates that our method enjoys improved recommendation accuracy. Citing Literature Volume25, Issue2March 2016Pages 334-340 RelatedInformation