A Deep Latent Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems
文献类型：期刊论文
| 作者 | Wu, Di2,3,4 ; Luo, Xin2,3,5; Shang, Mingsheng2,3; He, Yi6; Wang, Guoyin2,3; Zhou, MengChu1,7
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| 刊名 | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
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| 出版日期 | 2021-07-01 |
| 卷号 | 51期号:7页码:4285-4296 |
| ISSN号 | 2168-2216 |
| 关键词 | Big data deep model high-dimensional and sparse (HiDS) matrix latent factor (LF) analysis recommender system (RS) |
| DOI | 10.1109/TSMC.2019.2931393 |
| 通讯作者 | Luo, Xin(luoxin21@cigit.ac.cn) |
| 英文摘要 | Recommender systems (RSs) commonly adopt a user-item rating matrix to describe users' preferences on items. With users and items exploding, such a matrix is usually high-dimensional and sparse (HiDS). Recently, the idea of deep learning has been applied to RSs. However, current deep-structured RSs suffer from high computational complexity. Enlightened by the idea of deep forest, this paper proposes a deep latent factor model (DLFM) for building a deep-structured RS on an HiDS matrix efficiently. Its main idea is to construct a deep-structured model by sequentially connecting multiple latent factor (LF) models instead of multilayered neural networks through a nonlinear activation function. Thus, the computational complexity grows linearly with its layer count, which is easy to resolve in practice. The experimental results on four HiDS matrices from industrial RSs demonstrate that when compared with state-of-the-art LF models and deep-structured RSs, DLFM can well balance the prediction accuracy and computational efficiency, which well fits the desire of industrial RSs for fast and right recommendations. |
| 资助项目 | National Key Research and Development Program of China[2017YFC0804002] ; National Natural Science Foundation of China[61702475] ; National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[91646114] ; Chongqing Basic Research and Frontier Exploration[cstc2019jcyj-msxm1750] ; Chongqing Overseas Scholars Innovation Program[cx2017012] ; Chongqing Overseas Scholars Innovation Program[cx2018011] ; Chongqing Research Program of Key Standard Technologies Innovation of Key Industries[cstc2017zdcy-zdyfX0076] ; Chongqing Research Program of Key Standard Technologies Innovation of Key Industries[cstc2018jszx-cyztzxX0025] ; 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] ; 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:000672729600025 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/13768]  |
| 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.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA 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 Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China 4.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 5.Hong Kong Polytech Univ, Dept Comp, Hong Kong 999077, Peoples R China 6.Univ Louisiana Lafayette, Sch Comp & Informat, Lafayette, LA 70503 USA 7.King Abdulaziz Univ, Ctr Res Excellence Renewable Energy & Power Syst, Jeddah 21589, Saudi Arabia |
| 推荐引用方式 GB/T 7714 | Wu, Di,Luo, Xin,Shang, Mingsheng,et al. A Deep Latent Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2021,51(7):4285-4296. |
| APA | Wu, Di,Luo, Xin,Shang, Mingsheng,He, Yi,Wang, Guoyin,&Zhou, MengChu.(2021).A Deep Latent Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,51(7),4285-4296. |
| MLA | Wu, Di,et al."A Deep Latent Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 51.7(2021):4285-4296. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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