Latent Factor-Based Recommenders Relying on Extended Stochastic Gradient Descent Algorithms

doi:10.1109/TSMC.2018.2884191

	Latent Factor-Based Recommenders Relying on Extended Stochastic Gradient Descent Algorithms
	Luo, Xin 1; Wang, Dexian 2,3; Zhou, MengChu 4,5; Yuan, Huaqiang 1
刊名	IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
	2021-02-01
卷号	51 期号:2 页码:916-926
关键词	Big data bi-linear collaborative filtering (CF) high-dimensional and sparse (HiDS) matrix industry latent factor (LF) analysis missing data recommender system
ISSN号	2168-2216
DOI	10.1109/TSMC.2018.2884191
通讯作者	Zhou, MengChu(zhou@njit.edu) ; Yuan, Huaqiang(yuanhq@dgut.edu.cn)
英文摘要	High-dimensional and sparse (HiDS) matrices generated by recommender systems contain rich knowledge regarding various desired patterns like users' potential preferences and community tendency. Latent factor (LF) analysis proves to be highly efficient in extracting such knowledge from an HiDS matrix efficiently. Stochastic gradient descent (SGD) is a highly efficient algorithm for building an LF model. However, current LF models mostly adopt a standard SGD algorithm. Can SGD be extended from various aspects in order to improve the resultant models' convergence rate and prediction accuracy for missing data? Are such SGD extensions compatible with an LF model? To answer them, this paper carefully investigates eight extended SGD algorithms to propose eight novel LF models. Experimental results on two HiDS matrices generated by real recommender systems show that compared with an LF model with a standard SGD algorithm, an LF model with extended ones can achieve: 1) higher prediction accuracy for missing data; 2) faster convergence rate; and 3) model diversity.
资助项目	National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[91646114] ; Chongqing Research Program of Technology Innovation and Application[cstc2017rgzn-zdyfX0020] ; Chongqing Research Program of Technology Innovation and Application[cstc2017zdcy-zdyf0554] ; Chongqing Research Program of Technology Innovation and Application[cstc2017rgzn-zdyf0118] ; Chongqing Cultivation Program of Innovation and Entrepreneurship Demonstration Group[cstc2017kjrc-cxcytd0149] ; Chongqing Overseas Scholars Innovation Program[cx2017012] ; Chongqing Overseas Scholars Innovation Program[cx2018011] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences
WOS研究方向	Automation & Control Systems ; Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000608693000024
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/12769]
专题	中国科学院重庆绿色智能技术研究院
通讯作者	Zhou, MengChu; Yuan, Huaqiang
作者单位	1.Dongguan Univ Technol, Sch Comp Sci & Technol, Dongguan 523808, Peoples R China 2.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China 3.Chinese Acad Sci, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 4.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA 5.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China
推荐引用方式 GB/T 7714	Luo, Xin,Wang, Dexian,Zhou, MengChu,et al. Latent Factor-Based Recommenders Relying on Extended Stochastic Gradient Descent Algorithms[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2021,51(2):916-926.
APA	Luo, Xin,Wang, Dexian,Zhou, MengChu,&Yuan, Huaqiang.(2021).Latent Factor-Based Recommenders Relying on Extended Stochastic Gradient Descent Algorithms.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,51(2),916-926.
MLA	Luo, Xin,et al."Latent Factor-Based Recommenders Relying on Extended Stochastic Gradient Descent Algorithms".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 51.2(2021):916-926.