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Elastic-net regularized latent factor analysis-based models for recommender systems

文献类型：期刊论文

作者Wang, Dexian1; Chen, Yanbin2,3; Guo, Junxiao2,3; Shi, Xiaoyu2,3; He, Chunlin4; Luo, Xin1; Yuan, Huaqiang1
刊名NEUROCOMPUTING
出版日期2019-02-15
卷号329页码:66-74
关键词Big data Recommender systems Collaborative filtering Latent factor analysis Elastic-net Regularization Latent factor distribution
ISSN号0925-2312
DOI10.1016/j.neucom.2018.10.046
通讯作者Luo, Xin(luoxin21@cigit.ac.cn) ; Yuan, Huaqiang(yuanhq@dgut.edu.cn)
英文摘要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.
资助项目National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[91646114] ; National Natural Science Foundation of China[61602434] ; National Key Research and Development Program of China[2017YFC0804002] ; Chongqing Cultivation Program of Innovation and Entrepreneurship Demonstration Group[cstc2017kjrc-cxcytd0149] ; Chongqing Research Program of Technology Innovation and Application[cstc2017rgzn-zdyfX0020] ; Chongqing Research Program of Technology Innovation and Application[cstc2017zdcy-zdyf0554] ; Chongqing Research Program of Technology Innovation and Application[cstc2017rgzn-zdyf0118] ; Chongqing Overseas Scholars Innovation Program[cx2017012] ; Chongqing Overseas Scholars Innovation Program[cx2018011] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000453924300007
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.138/handle/2HOD01W0/7197]  
专题大数据挖掘及应用中心
通讯作者Luo, Xin; Yuan, Huaqiang
作者单位1.Dongguan Univ Technol, Sch Comp Sci & Network Secur, Dongguan 523808, Guangdong, Peoples R China
2.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China
3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
4.China West Normal Univ, Comp Sch, Nanchong 637002, Sichuan, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Dexian,Chen, Yanbin,Guo, Junxiao,et al. Elastic-net regularized latent factor analysis-based models for recommender systems[J]. NEUROCOMPUTING,2019,329:66-74.
APA Wang, Dexian.,Chen, Yanbin.,Guo, Junxiao.,Shi, Xiaoyu.,He, Chunlin.,...&Yuan, Huaqiang.(2019).Elastic-net regularized latent factor analysis-based models for recommender systems.NEUROCOMPUTING,329,66-74.
MLA Wang, Dexian,et al."Elastic-net regularized latent factor analysis-based models for recommender systems".NEUROCOMPUTING 329(2019):66-74.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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