KMS Chongqing Institute of Green and Intelligent Technology, CAS
A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model | |
Li, Jinli1; Yuan, Ye2,3,4; Ruan, Tao5; Chen, Jia6; Luo, Xin1,7 | |
2021-02-28 | |
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. |
关键词 | Big data Stochastic gradient descent Proportional integral derivation PID controller High-dimensional and sparse matrix Latent factor analysis |
DOI | 10.1016/j.neucom.2020.11.029 |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
卷号 | 427页码:29-39 |
通讯作者 | Luo, Xin(luoxin21@gmail.com) |
收录类别 | SCI |
WOS记录号 | WOS:000611067800003 |
语种 | 英语 |