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A Novel Approach to Extracting Non-Negative Latent Factors From Non-Negative Big Sparse Matrices

文献类型：期刊论文

作者Luo, Xin1,2; Zhou, Mengchu3,4; Shang, Mingsheng1,2; Li, Shuai5; Xia, Yunni6
刊名IEEE ACCESS
出版日期2016
卷号4页码:2649-2655
关键词Latent factors non-negativity matrix factorization non-negative big sparse matrix big data recommender system
ISSN号2169-3536
DOI10.1109/ACCESS.2016.2556680
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.
英文摘要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.
资助项目Fundo para o Desenvolvimento das Ciencias e da Tecnologia[119/2014/A3] ; National Natural Science Foundation of China[61370150] ; National Natural Science Foundation of China[61433014] ; Young Scientist Foundation of Chongqing[cstc2014kjrc-qnrc40005] ; Chongqing Research Program of Basic Research and Frontier Technology[cstc2015jcyjB0244] ; Post-Doctoral Science Funded Project of Chongqing[Xm2014043] ; Fundamental Research Funds for the Central Universities[106112015CDJXY180005] ; Specialized Research Fund for the Doctoral Program of Higher Education[20120191120030]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000379759300015
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.138/handle/2HOD01W0/2669]  
专题大数据挖掘及应用中心
通讯作者Luo, Xin; Zhou, Mengchu
作者单位1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
2.Shenzhen Univ, Shenzhen Engn Lab Mobile Internet Applicat Middle, Shenzhen 518060, Peoples R China
3.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China
4.New Jersey Inst Technol, Helen & John C Hartmann Dept Elect & Comp Engn, Newark, NJ 07102 USA
5.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China
6.Chongqing Univ, Coll Comp Sci, Chongqing Key Lab Software Theory & Technol, Chongqing 400044, Peoples R China
推荐引用方式
GB/T 7714		 Luo, Xin,Zhou, Mengchu,Shang, Mingsheng,et al. A Novel Approach to Extracting Non-Negative Latent Factors From Non-Negative Big Sparse Matrices[J]. IEEE ACCESS,2016,4:2649-2655.
APA Luo, Xin,Zhou, Mengchu,Shang, Mingsheng,Li, Shuai,&Xia, Yunni.(2016).A Novel Approach to Extracting Non-Negative Latent Factors From Non-Negative Big Sparse Matrices.IEEE ACCESS,4,2649-2655.
MLA Luo, Xin,et al."A Novel Approach to Extracting Non-Negative Latent Factors From Non-Negative Big Sparse Matrices".IEEE ACCESS 4(2016):2649-2655.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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