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	Hyper-parameter-evolutionary latent factor analysis for high-dimensional and sparse data from recommender systems
	Chen, Jiufang1,7; Yuan, Ye2,3,4; Ruan, Tao5; Chen, Jia6; Luo, Xin1,8
	2021-01-15
摘要	High-dimensional and Sparse (HiDS) data generated by recommender systems (RSs) contain rich knowledge regarding users' potential preferences. A Latent factor analysis (LFA) model enables efficient extraction of essential features from such data. However, an LFA model relies heavily on its hyper-parameters like learning rate and regularization coefficient, which must be chosen with care. However, traditional grid-search-based manual tuning is extremely time-consuming and computationally expensive. To address this issue, this study proposes a hyper-parameter-evolutionary latent factor analysis (HLFA) model. Its main idea is to build a swarm by taking the hyper-parameters of every single LFA-based model as particles, and then apply particle swarm optimization (PSO) to make its both hyper-parameters, i.e., the learning rate and regularization coefficient, self-adaptive according to a pre-defined fitness function. Experimental results on six HiDS matrices from real RSs indicate that an HLFA model outperforms several state-of-the-art LF models in terms of computational efficiency, and most importantly, without loss of prediction accuracy for missing data of an HiDS matrix. (c) 2020 Elsevier B.V. All rights reserved.
关键词	Big Data Intelligent Computation Latent Factor Analysis Evolutionary Computing Learning Algorithm High-dimensional and Sparse Data Parameter Free
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	421页码:316-328
通讯作者	Luo, Xin(luoxin21@gmail.com)
收录类别	SCI
WOS记录号	WOS:000593102500011
语种	英语