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A momentum-incorporated latent factorization of tensors model for temporal-aware QoS missing data prediction

文献类型：期刊论文

作者Wang, Qingxian5; Chen, Minzhi4; Shang, Mingsheng1,2,3; Luo, Xin1,2,3
刊名NEUROCOMPUTING
出版日期2019-11-20
卷号367页码:299-307
关键词Big Data QoS prediction Temporal-aware QoS prediction Stochastic gradient descent Latent factorization of tensors Momentum method
ISSN号0925-2312
DOI10.1016/j.neucom.2019.08.026
通讯作者Luo, Xin(luoxin21@cigit.ac.cn)
英文摘要Quality-of-service (QoS) of Web services vary over time, making it a significant issue to discover temporal patterns from them for addressing various subsequent analyzing tasks like missing QoS prediction. A Latent factorization of tensors (LFT)-based approach proves to be highly efficient in addressing this issue, which can be built through a stochastic gradient descent (SGD) solver efficiently. However, an SGD-based LFT model frequently suffers low-tail convergence. For addressing this issue, we present a momentum-incorporated latent factorization of tensors (MLFT) model, which integrates a momentum method into an SGD-based LFT model, thereby improving its convergence rate as well as maintaining the prediction accuracy for missing QoS data. Empirical studies on two dynamic industrial QoS datasets show that compared with an SGD-based LFT model, an MLFT model achieves faster convergence rate and higher prediction accuracy. (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] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences[CAS-PHP-CIGIT-001]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000489017500028
出版者ELSEVIER
源URL[http://119.78.100.138/handle/2HOD01W0/9648]  
专题大数据挖掘及应用中心
通讯作者Luo, Xin
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
3.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
4.China West Normal Univ, Comp Sch, Nanchong 637002, Sichuan, Peoples R China
5.Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Sichuan, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Qingxian,Chen, Minzhi,Shang, Mingsheng,et al. A momentum-incorporated latent factorization of tensors model for temporal-aware QoS missing data prediction[J]. NEUROCOMPUTING,2019,367:299-307.
APA Wang, Qingxian,Chen, Minzhi,Shang, Mingsheng,&Luo, Xin.(2019).A momentum-incorporated latent factorization of tensors model for temporal-aware QoS missing data prediction.NEUROCOMPUTING,367,299-307.
MLA Wang, Qingxian,et al."A momentum-incorporated latent factorization of tensors model for temporal-aware QoS missing data prediction".NEUROCOMPUTING 367(2019):299-307.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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