A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model

doi:10.1016/j.neucom.2020.11.029

	A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model
	Li, Jinli 5; Yuan, Ye 1,6,7; Ruan, Tao 2; Chen, Jia 4; Luo, Xin 3,5
刊名	NEUROCOMPUTING
	2021-02-28
卷号	427 页码:29-39
关键词	Big data Stochastic gradient descent Proportional integral derivation PID controller High-dimensional and sparse matrix Latent factor analysis
ISSN号	0925-2312
DOI	10.1016/j.neucom.2020.11.029
通讯作者	Luo, Xin(luoxin21@gmail.com)
英文摘要	Large-scale relationships like user-item preferences in a recommender system are mostly described by a high-dimensional and sparse (HiDS) matrix. A latent factor analysis (LFA) model extracts useful knowledge from an HiDS matrix efficiently, where stochastic gradient descent (SGD) is frequently adopted as the learning algorithm. However, a standard SGD algorithm updates a decision parameter with the stochastic gradient on the instant loss only, without considering information described by prior updates. Hence, an SGD-based LFA model commonly consumes many iterations to converge, which greatly affects its practicability. On the other hand, a proportional-integral-derivative (PID) controller makes a learning model converge fast with the consideration of its historical errors from the initial state till the current moment. Motivated by this discovery, this paper proposes a PID-incorporated SGD-based LFA (PSL) model. Its main idea is to rebuild the instant error on a single instance following the principle of PID, and then substitute this rebuilt error into an SGD algorithm for accelerating model convergence. Empirical studies on six widely-accepted HiDS matrices indicate that compared with state-of-the-art LFA models, a PSL model achieves significantly higher computational efficiency as well as highly competitive prediction accuracy for missing data of an HiDS matrix. (c) 2020 Elsevier B.V. All rights reserved.
资助项目	National Natural Science Foundation of China[61772493] ; Guangdong Province Universities and College Pearl River Scholar Funded Scheme (2019) ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013]
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000611067800003
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/12799]
专题	中国科学院重庆绿色智能技术研究院
通讯作者	Luo, Xin
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.China Patent Informat Ctr, Beijing 100088, Peoples R China 3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 4.Beihang Univ, Sch Cyber Sci & Technol, Beijing 100191, Peoples R China 5.Dongguan Univ Technol, Sch Comp Sci & Technol, Dongguan 523808, Guangdong, Peoples R China 6.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China 7.Chinese Acad Sci, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
推荐引用方式 GB/T 7714	Li, Jinli,Yuan, Ye,Ruan, Tao,et al. A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model[J]. NEUROCOMPUTING,2021,427:29-39.
APA	Li, Jinli,Yuan, Ye,Ruan, Tao,Chen, Jia,&Luo, Xin.(2021).A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model.NEUROCOMPUTING,427,29-39.
MLA	Li, Jinli,et al."A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model".NEUROCOMPUTING 427(2021):29-39.