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A Posterior-Neighborhood-Regularized Latent Factor Model for Highly Accurate Web Service QoS Prediction

文献类型：期刊论文

作者Wu, Di3,4,5; He, Qiang1; Luo, Xin3,4,6,7; Shang, Mingsheng3,4; He, Yi2; Wang, Guoyin3,4
刊名IEEE TRANSACTIONS ON SERVICES COMPUTING
出版日期2022-03-01
卷号15期号:2页码:793-805
关键词Web service quality-of-service latent factor analysis posterior-neighborhood regularization cloud computing big data
ISSN号1939-1374
DOI10.1109/TSC.2019.2961895
通讯作者Luo, Xin(luoxin21@cigit.ac.cn)
英文摘要Neighborhood regularization is highly important for a latent factor (LF)-based Quality-of-Service (QoS)-predictor since similar users usually experience similar QoS when invoking similar services. Current neighborhood-regularized LF models rely prior information on neighborhood obtained from common raw QoS data or geographical information. The former suffers from low prediction accuracy due to the difficulty of constructing the neighborhood based on incomplete QoS data, while the latter requires additional geographical information that is usually difficult to collect considering information security, identity privacy, and commercial interests in real-world scenarios. To address the above issues, this work proposes a posterior-neighborhood-regularized LF (PLF) model for QoS prediction. The main idea is to decompose the LF analysis process into three phases: a) primal LF extraction, where the LFs are extracted to represent involved users/services based on known QoS data, b) posterior-neighborhood construction, where the neighborhood of each user/service is achieved based on similarities between their primal LF vectors, and c) posterior-neighborhood-regularized LF analysis, where the objective function is regularized by both the posterior-neighborhood of users/services and L-2-norm of desired LFs. Experimental results from large scale QoS datasets demonstrate that PLF outperforms state-of-the-art models in terms of both accuracy and efficiency.
资助项目National Natural Science Foundation of China[61702475] ; National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[61902370] ; National Natural Science Foundation of China[91646114] ; National Natural Science Foundation of China[61602434] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyj-msxmX0578] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000779610600017
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.138/handle/2HOD01W0/15487]  
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.Swinburne Univ Technol, Sch Software & Elect Engn, Melbourne, Vic 3122, Australia
2.Univ Louisiana Lafayette, Lafayette, LA 70503 USA
3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China
4.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
6.Hengrui Chongqing Artificial Intelligence Res Ctr, Chongqing 401331, Peoples R China
7.Cloudwalk, Dept Big Data Anal Tech, Chongqing 401331, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Di,He, Qiang,Luo, Xin,et al. A Posterior-Neighborhood-Regularized Latent Factor Model for Highly Accurate Web Service QoS Prediction[J]. IEEE TRANSACTIONS ON SERVICES COMPUTING,2022,15(2):793-805.
APA Wu, Di,He, Qiang,Luo, Xin,Shang, Mingsheng,He, Yi,&Wang, Guoyin.(2022).A Posterior-Neighborhood-Regularized Latent Factor Model for Highly Accurate Web Service QoS Prediction.IEEE TRANSACTIONS ON SERVICES COMPUTING,15(2),793-805.
MLA Wu, Di,et al."A Posterior-Neighborhood-Regularized Latent Factor Model for Highly Accurate Web Service QoS Prediction".IEEE TRANSACTIONS ON SERVICES COMPUTING 15.2(2022):793-805.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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