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	Elastic-net regularized latent factor analysis-based models for recommender systems
	Wang, Dexian1; Chen, Yanbin2,3; Guo, Junxiao2,3; Shi, Xiaoyu2,3; He, Chunlin4; Luo, Xin1; Yuan, Huaqiang1
	2019-02-15
摘要	Latent factor analysis (LFA)-based models are highly efficient in recommender systems. The problem of LFA is defined on high-dimensional and sparse (HiDS) matrices corresponding to relationships among numerous entities in industrial applications. It is ill-posed without a unique and optimal solution, making regularization vital in improving the generality of an LFA-based model. Current models mostly adopt l(2) norm-based regularization, which cannot regularize the latent factor distributions. For addressing this issue, this work applies the elastic-net-based regularization to an LFA-based model, thereby achieving an elastic-net regularized latent factor analysis-based (ERLFA) model. We further adopt two efficient learning algorithms, i.e., forward-looking sub-gradients and forward-backward splitting and stochastic proximal gradient descent, to train desired latent factors in an ERLFA-based model, resulting in two novel ERLFA-based models relying on different learning schemes. Experimental results on four large industrial datasets show that by regularizing the latent factor distribution, the proposed ERLFA-based models are able to achieve high prediction accuracy for missing data of an HiDS matrix without additional computational burden. (C) 2018 Elsevier B.V. All rights reserved.
关键词	Big data Recommender systems Collaborative filtering Latent factor analysis Elastic-net Regularization Latent factor distribution
DOI	10.1016/j.neucom.2018.10.046
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	329页码:66-74
收录类别	SCI
WOS记录号	WOS:000453924300007
语种	英语