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A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction

文献类型：期刊论文

作者Wu, Di2,3,4; Luo, Xin1,5; Shang, Mingsheng2,3; He, Yi6; Wang, Guoyin7; Wu, Xindong8,9
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2022-06-01
卷号34期号:6页码:2525-2538
ISSN号1041-4347
关键词Web Service quality-of-service QoS latent factor analysis density peak data-characteristic-aware missing data big data topological neighborhood noise data service selection data science
DOI10.1109/TKDE.2020.3014302
通讯作者Luo, Xin(luoxin21@gmail.com)
英文摘要How to accurately predict unknown quality-of-service (QoS) data based on observed ones is a hot yet thorny issue in Web service-related applications. Recently, a latent factor (LF) model has shown its efficiency in addressing this issue owing to its high accuracy and scalability. An LF model can be improved by identifying user and service neighborhoods based on user and service geographical information. However, such information can be difficult to acquire in most applications with the considerations of information security, identity privacy, and commercial interests in a real system. Besides, the existing LF model-based QoS predictors mostly ignore the reliability of given QoS data where noises commonly exist to cause accuracy loss. To address the above issues, this paper proposes a data-characteristic-aware latent factor (DCALF) model to implement highly accurate QoS predictions, where 'data-characteristic-aware' indicates that it can appropriately implement QoS prediction according to the characteristics of given QoS data. Its main idea is two-fold: a) it detects the neighborhoods and noises of users and services based on the dense LFs extracted from the original sparse QoS data, b) it incorporates a density peaks-based clustering method into its modeling process for achieving the simultaneous detections of both neighborhoods and noises of QoS data. With such designs, it precisely represents the given QoS data in spite of their sparsity, thereby achieving highly accurate predictions for unknown ones. Experimental results on two QoS datasets generated by real-world Web services demonstrate that the proposed DCALF model outperforms state-of-the-art QoS predictors, making it highly competitive in addressing the issue of Web service selection and recommendation.
资助项目National Key Research and Development Program of China[2016YFB 1000901] ; National Natural Science Foundation of China[61702475] ; National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[91746209] ; National Natural Science Foundation of China[61902370] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjmsxmX0578] ; Guangdong Province Universities and College Pearl River Scholar Funded Scheme (2019) ; CAS Light of West China Program ; Pioneer Hundred Talents Program of Chinese Academy of Sciences
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE COMPUTER SOC
WOS记录号WOS:000789003800001
源URL[http://119.78.100.138/handle/2HOD01W0/15888]  
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.Dongguan Univ Technol, Sch Comp Sci & Technol, Dongguan 523808, Guangdong, Peoples R China
2.Chongqing Engn Res Ctr Big Data Applicat Smart Ci, 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, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
6.Univ Louisiana Lafayette, Lafayette, LA 70503 USA
7.Chongqing Univ Posts & Telecommun, Dept Chongqing, Key Lab Computat Intelligence, Chongqing 400065, Peoples R China
8.Hefei Univ Technol, Key Lab Knowledge Engn Big Data, Minist Educ, Hefei 230009, Peoples R China
9.Mininglamp Acad Sci, Mininglamp Technol, Beijing 100084, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Di,Luo, Xin,Shang, Mingsheng,et al. A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2022,34(6):2525-2538.
APA Wu, Di,Luo, Xin,Shang, Mingsheng,He, Yi,Wang, Guoyin,&Wu, Xindong.(2022).A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,34(6),2525-2538.
MLA Wu, Di,et al."A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 34.6(2022):2525-2538.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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