Latent Factor-Based Recommenders Relying on Extended Stochastic Gradient Descent Algorithms
文献类型：期刊论文
| 作者 | Luo, Xin3 ; Wang, Dexian1,4; Zhou, MengChu2,5; Yuan, Huaqiang3
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| 刊名 | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
 |
| 出版日期 | 2021-02-01 |
| 卷号 | 51期号:2页码:916-926 |
| ISSN号 | 2168-2216 |
| 关键词 | Big data bi-linear collaborative filtering (CF) high-dimensional and sparse (HiDS) matrix industry latent factor (LF) analysis missing data recommender system |
| DOI | 10.1109/TSMC.2018.2884191 |
| 通讯作者 | Zhou, MengChu(zhou@njit.edu) ; Yuan, Huaqiang(yuanhq@dgut.edu.cn) |
| 英文摘要 | High-dimensional and sparse (HiDS) matrices generated by recommender systems contain rich knowledge regarding various desired patterns like users' potential preferences and community tendency. Latent factor (LF) analysis proves to be highly efficient in extracting such knowledge from an HiDS matrix efficiently. Stochastic gradient descent (SGD) is a highly efficient algorithm for building an LF model. However, current LF models mostly adopt a standard SGD algorithm. Can SGD be extended from various aspects in order to improve the resultant models' convergence rate and prediction accuracy for missing data? Are such SGD extensions compatible with an LF model? To answer them, this paper carefully investigates eight extended SGD algorithms to propose eight novel LF models. Experimental results on two HiDS matrices generated by real recommender systems show that compared with an LF model with a standard SGD algorithm, an LF model with extended ones can achieve: 1) higher prediction accuracy for missing data; 2) faster convergence rate; and 3) model diversity. |
| 资助项目 | National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[91646114] ; 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 Cultivation Program of Innovation and Entrepreneurship Demonstration Group[cstc2017kjrc-cxcytd0149] ; 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 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000608693000024 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/12769]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Zhou, MengChu; Yuan, Huaqiang |
| 作者单位 | 1.Chinese Acad Sci, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 2.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China 3.Dongguan Univ Technol, Sch Comp Sci & Technol, Dongguan 523808, Peoples R China 4.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China 5.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA |
| 推荐引用方式 GB/T 7714 | Luo, Xin,Wang, Dexian,Zhou, MengChu,et al. Latent Factor-Based Recommenders Relying on Extended Stochastic Gradient Descent Algorithms[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2021,51(2):916-926. |
| APA | Luo, Xin,Wang, Dexian,Zhou, MengChu,&Yuan, Huaqiang.(2021).Latent Factor-Based Recommenders Relying on Extended Stochastic Gradient Descent Algorithms.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,51(2),916-926. |
| MLA | Luo, Xin,et al."Latent Factor-Based Recommenders Relying on Extended Stochastic Gradient Descent Algorithms".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 51.2(2021):916-926. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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