中国科学院重庆绿色智能技术研究院机构知识库

QQ客服 官方微博 反馈留言

	A Novel Approach to Extracting Non-Negative Latent Factors From Non-Negative Big Sparse Matrices
	Luo, Xin1,2; Zhou, Mengchu3,4; Shang, Mingsheng1,2; Li, Shuai5; Xia, Yunni6
	2016
摘要	An inherently non-negative latent factor model is proposed to extract non-negative latent factors from non-negative big sparse matrices efficiently and effectively. A single-element-dependent sigmoid function connects output latent factors with decision variables, such that non-negativity constraints on the output latent factors are always fulfilled and thus successfully separated from the training process with respect to the decision variables. Consequently, the proposed model can be easily and fast built with excellent prediction accuracy. Experimental results on an industrial size sparse matrix are given to verify its outstanding performance and suitability for industrial applications.
关键词	Latent factors non-negativity matrix factorization non-negative big sparse matrix big data recommender system
DOI	10.1109/ACCESS.2016.2556680
发表期刊	IEEE ACCESS
ISSN	2169-3536
卷号	4页码:2649-2655
通讯作者	Luo, X (reprint author), Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China. ; Luo, X (reprint author), Shenzhen Univ, Shenzhen Engn Lab Mobile Internet Applicat Middle, Shenzhen 518060, Peoples R China. ; Zhou, MC (reprint author), Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China. ; Zhou, MC (reprint author), New Jersey Inst Technol, Helen & John C Hartmann Dept Elect & Comp Engn, Newark, NJ 07102 USA.
收录类别	SCI
WOS记录号	WOS:000379759300015
语种	英语