Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data

doi:10.1109/TCYB.2017.2685521

CORC > 重庆绿色智能技术研究院 > 中国科学院重庆绿色智能技术研究院 > 大数据挖掘及应用中心

	Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data
	Luo, Xin1,2 ; Zhou, MengChu 3,4; Li, Shuai 5; Xia, Yunni 6,7; You, Zhu-Hong 5; Zhu, QingSheng 6,7; Leung, Hareton 5
刊名	IEEE TRANSACTIONS ON CYBERNETICS
	2018-04-01
卷号	48 期号:4 页码:1216-1228
关键词	Big data latent factor model missing data prediction quality-of-service (QoS) second-order solver service computing sparse matrices Web service
ISSN号	2168-2267
DOI	10.1109/TCYB.2017.2685521
英文摘要	Generating highly accurate predictions for missing quality-of-service (QoS) data is an important issue. Latent factor (LF)-based QoS-predictors have proven to be effective in dealing with it. However, they are based on first-order solvers that cannot well address their target problem that is inherently bilinear and nonconvex, thereby leaving a significant opportunity for accuracy improvement. This paper proposes to incorporate an efficient second-order solver into them to raise their accuracy. To do so, we adopt the principle of Hessian-free optimization and successfully avoid the direct manipulation of a Hessian matrix, by employing the efficiently obtainable product between its Gauss-Newton approximation and an arbitrary vector. Thus, the second-order information is innovatively integrated into them. Experimental results on two industrial QoS datasets indicate that compared with the state-of-the-art predictors, the newly proposed one achieves significantly higher prediction accuracy at the expense of affordable computational burden. Hence, it is especially suitable for industrial applications requiring high prediction accuracy of unknown QoS data.
资助项目	Pioneer Hundred Talents Program of Chinese Academy of Sciences ; Royal Society of the U.K.[61611130209] ; National Natural Science Foundation of China[61611130209] ; National Natural Science Foundation of China[61370150] ; National Natural Science Foundation of China[61433014] ; National Natural Science Foundation of China[61402198] ; Young Scientist Foundation of Chongqing[cstc2014kjrc-qnrc40005] ; Fundamental Research Funds for the Central Universities[106112016CDJXY180005] ; Fundamental Research Funds for the Central Universities[CDJZR12180012]
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:000427426000009
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/6275]
专题	大数据挖掘及应用中心
通讯作者	Luo, Xin; Zhou, MengChu
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.Shenzhen Univ, Coll Comp Sci & Engn, Shenzhen 518060, Peoples R China 3.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA 4.King Abdulaziz Univ, Renewable Energy Res Grp, Jeddah, Saudi Arabia 5.Hong Kong Polytech Univ, Dept Comp, Hong Kong 999077, Hong Kong, Peoples R China 6.Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China 7.Chongqing Univ, Chongqing Key Lab Software Theory & Technol, Chongqing 400044, Peoples R China
推荐引用方式 GB/T 7714	Luo, Xin,Zhou, MengChu,Li, Shuai,et al. Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data[J]. IEEE TRANSACTIONS ON CYBERNETICS,2018,48(4):1216-1228.
APA	Luo, Xin.,Zhou, MengChu.,Li, Shuai.,Xia, Yunni.,You, Zhu-Hong.,...&Leung, Hareton.(2018).Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data.IEEE TRANSACTIONS ON CYBERNETICS,48(4),1216-1228.
MLA	Luo, Xin,et al."Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data".IEEE TRANSACTIONS ON CYBERNETICS 48.4(2018):1216-1228.