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

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 adop...

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Bibliographic Details

Published in:	Neurocomputing (Amsterdam) Vol. 427; pp. 29 - 39
Main Authors:	Li, Jinli, Yuan, Ye, Ruan, Tao, Chen, Jia, Luo, Xin
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 28.02.2021
Subjects:	Big data High-dimensional and sparse matrix Latent factor analysis PID controller Proportional integral derivation Stochastic gradient descent PID controller Big data Stochastic gradient descent Proportional integral derivation Latent factor analysis High-dimensional and sparse matrix
ISSN:	0925-2312, 1872-8286
Online Access:	Get full text
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