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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Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2020.11.029