Hyper-parameter-evolutionary latent factor analysis for high-dimensional and sparse data from recommender systems

	Hyper-parameter-evolutionary latent factor analysis for high-dimensional and sparse data from recommender systems
	Chen, Jiufang 7,8; Yuan, Ye 2,3,4; Ruan, Tao 5; Chen, Jia 6; Luo, Xin 1,8
刊名	NEUROCOMPUTING
	2021-01-15
卷号	421 页码:316-328
关键词	Big Data Intelligent Computation Latent Factor Analysis Evolutionary Computing Learning Algorithm High-dimensional and Sparse Data Parameter Free
ISSN号	0925-2312
通讯作者	Luo, Xin(luoxin21@gmail.com)
英文摘要	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.
资助项目	National Natural Science Foundation of China[61772493] ; Guangdong Province Universities and College Pearl River Scholar Funded Scheme ; Natural Science Foundation of Chongqing (China)[cstc2019j-cyjjqX0013]
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000593102500011
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/12382]
专题	中国科学院重庆绿色智能技术研究院
通讯作者	Luo, Xin
作者单位	1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 2.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 3.Chinese Acad Sci, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.China Patent Informat Ctr, Beijing 100088, Peoples R China 6.Beihang Univ, Sch Cyber Sci & Technol, Beijing 100191, Peoples R China 7.China West Normal Univ, Sch Comp Sci, Nanchong 637002, Sichuan, Peoples R China 8.Dongguan Univ Technol, Sch Comp Sci & Technol, Dongguan 523808, Guangdong, Peoples R China
推荐引用方式 GB/T 7714	Chen, Jiufang,Yuan, Ye,Ruan, Tao,et al. Hyper-parameter-evolutionary latent factor analysis for high-dimensional and sparse data from recommender systems[J]. NEUROCOMPUTING,2021,421:316-328.
APA	Chen, Jiufang,Yuan, Ye,Ruan, Tao,Chen, Jia,&Luo, Xin.(2021).Hyper-parameter-evolutionary latent factor analysis for high-dimensional and sparse data from recommender systems.NEUROCOMPUTING,421,316-328.
MLA	Chen, Jiufang,et al."Hyper-parameter-evolutionary latent factor analysis for high-dimensional and sparse data from recommender systems".NEUROCOMPUTING 421(2021):316-328.