An adaptive latent factor model via particle swarm optimization
文献类型：期刊论文
| 作者 | Wang, Qingxian2; Chen, Sili1; Luo, Xin3,4
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| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2019-12-05 |
| 卷号 | 369页码:176-184 |
| 关键词 | Latent factor analysis Particle swarm optimization High-dimensional and sparse matrix Stochastic gradient descent Self-adaptive model |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2019.08.052 |
| 通讯作者 | Luo, Xin(luoxin21@cigit.ac.cn) |
| 英文摘要 | Latent factor (LF) models are greatly efficient in extracting valuable knowledge from High-Dimensional and Sparse (HiDS) matrices which are usually seen in many industrial applications. Stochastic gradient descent (SGD) is an effective algorithm to build an LF model, yet its convergence rate depends vastly on the learning rate which should be tuned with care. Therefore, automatic selection of an optimal learning rate for an SGD-based LF model is a meaningful issue. To address it, this study incorporates the principle of particle swarm optimization (PSO) into an SGD-based LF model for searching an optimal learning rate automatically. With it, we further propose an adaptive Latent Factor (ALF) model. Empirical studies on four HiDS matrices from real industrial applications indicate that an ALF model obvious outperforms an LF model according to convergence rate, and maintain competitive prediction accuracy for missing data. (C) 2019 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[51609229] ; National Natural Science Foundation of China[61872065] ; Chongqing Cultivation Program of Innovation and Entrepreneurship Demonstration Group[cstc2017kjrc-cxcytd0149] ; Chongqing Overseas Scholars Innovation Program[cx2017012] ; Chongqing Overseas Scholars Innovation Program[cx2018011] ; 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 Research Program of Technology Innovation and Application[cstc2018jszx-cyztzxX0025] ; 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:000492298500016 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/10322]  |
| 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.China West Normal Univ, Comp Sch, Nanchong 637002, Sichuan, Peoples R China 2.Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Sichuan, 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.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Qingxian,Chen, Sili,Luo, Xin. An adaptive latent factor model via particle swarm optimization[J]. NEUROCOMPUTING,2019,369:176-184. |
| APA | Wang, Qingxian,Chen, Sili,&Luo, Xin.(2019).An adaptive latent factor model via particle swarm optimization.NEUROCOMPUTING,369,176-184. |
| MLA | Wang, Qingxian,et al."An adaptive latent factor model via particle swarm optimization".NEUROCOMPUTING 369(2019):176-184. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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