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An alpha -beta -Divergence-Generalized Recommender for Highly Accurate Predictions of Missing User Preferences

文献类型：期刊论文

作者Shang, Mingsheng1,2; Yuan, Ye1,2,3; Luo, Xin1,2,4; Zhou, MengChu5,6,7
刊名IEEE TRANSACTIONS ON CYBERNETICS
出版日期2021-02-17
页码13
ISSN号2168-2267
关键词Computational modeling Sparse matrices Convergence Data models Predictive models Linear programming Euclidean distance -divergence big data convergence analysis high-dimensional and sparse (HiDS) data momentum machine learning missing data estimation non-negative latent factor analysis (NLFA) recommender system (RS)
DOI10.1109/TCYB.2020.3026425
通讯作者Luo, Xin(luoxin21@cigit.ac.cn)
英文摘要To quantify user-item preferences, a recommender system (RS) commonly adopts a high-dimensional and sparse (HiDS) matrix. Such a matrix can be represented by a non-negative latent factor analysis model relying on a single latent factor (LF)-dependent, non-negative, and multiplicative update algorithm. However, existing models' representative abilities are limited due to their specialized learning objective. To address this issue, this study proposes an alpha-beta-divergence-generalized model that enjoys fast convergence. Its ideas are three-fold: 1) generalizing its learning objective with alpha -beta -divergence to achieve highly accurate representation of HiDS data; 2) incorporating a generalized momentum method into parameter learning for fast convergence; and 3) implementing self-adaptation of controllable hyperparameters for excellent practicability. Empirical studies on six HiDS matrices from real RSs demonstrate that compared with state-of-the-art LF models, the proposed one achieves significant accuracy and efficiency gain to estimate huge missing data in an HiDS matrix.
资助项目National Natural Science Foundation of China[62002337] ; National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[61802360] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013] ; 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:000732284400001
源URL[http://119.78.100.138/handle/2HOD01W0/14764]  
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 Engn Res Ctr Big Data Applicat Smart Ci, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
3.Univ Chinese Acad Sci, Chongqing Coll, Beijing 100049, Peoples R China
4.Cloudwalk, Hengrui Chongqing Artificial Intelligence Res Ctr, Dept Big Data Anal Tech, Chongqing 401331, Peoples R China
5.Macau Univ Sci & Technol, Inst Syst Engn, Macau, Peoples R China
6.Macau Univ Sci & Technol, Collaborat Lab Intelligent Sci & Syst, Macau, Peoples R China
7.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA
推荐引用方式
GB/T 7714		 Shang, Mingsheng,Yuan, Ye,Luo, Xin,et al. An alpha -beta -Divergence-Generalized Recommender for Highly Accurate Predictions of Missing User Preferences[J]. IEEE TRANSACTIONS ON CYBERNETICS,2021:13.
APA Shang, Mingsheng,Yuan, Ye,Luo, Xin,&Zhou, MengChu.(2021).An alpha -beta -Divergence-Generalized Recommender for Highly Accurate Predictions of Missing User Preferences.IEEE TRANSACTIONS ON CYBERNETICS,13.
MLA Shang, Mingsheng,et al."An alpha -beta -Divergence-Generalized Recommender for Highly Accurate Predictions of Missing User Preferences".IEEE TRANSACTIONS ON CYBERNETICS (2021):13.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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