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A Fast Non-Negative Latent Factor Model Based on Generalized Momentum Method
Luo, Xin1; Liu, Zhigang2; Li, Shuai3; Shang, Mingsheng2; Wang, Zidong4
2021
摘要Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) is an efficient algorithm for building an NLF model on an HiDS matrix, yet it suffers slow convergence. A momentum method is frequently adopted to accelerate a learning algorithm, but it is incompatible with those implicitly adopting gradients like SLF-NMU. To build a fast NLF (FNLF) model, we propose a generalized momentum method compatible with SLF-NMU. With it, we further propose a single latent factor-dependent non-negative, multiplicative and momentum-incorporated update algorithm, thereby achieving an FNLF model. Empirical studies on six HiDS matrices from industrial application indicate that an FNLF model outperforms an NLF model in terms of both convergence rate and prediction accuracy for missing data. Hence, compared with an NLF model, an FNLF model is more practical in industrial applications.
关键词Big data high-dimensional and sparse (HiDS) matrix latent factor (LF) analysis missing data estimation non-negative LF (NLF) model recommender system
DOI10.1109/TSMC.2018.2875452
发表期刊IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
ISSN2168-2216
卷号51期号:1页码:610-620
通讯作者Luo, Xin(luoxin21@dgut.edu.cn)
收录类别SCI
WOS记录号WOS:000607806700046
语种英语