A Kalman-Filter-Incorporated Latent Factor Analysis Model for Temporally Dynamic Sparse Data
文献类型：期刊论文
| 作者 | Yuan, Ye1 ; Luo, Xin1; Shang, Mingsheng2,3; Wang, Zidong4
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| 刊名 | IEEE TRANSACTIONS ON CYBERNETICS
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| 出版日期 | 2022-07-25 |
| 页码 | 14 |
| 关键词 | Quality of service Data models Kalman filters Estimation Computational modeling Web services Heuristic algorithms Alternating least squares (ALSs) computational intelligence data science dynamic latent factor analysis (LFA) dynamics intelligent computing Kalman filter temporal pattern Web service |
| ISSN号 | 2168-2267 |
| DOI | 10.1109/TCYB.2022.3185117 |
| 通讯作者 | Luo, Xin(luoxin21@gmail.com) |
| 英文摘要 | With the rapid development of services computing in the past decade, Quality-of-Service (QoS)-aware selection of Web services has become a hot yet thorny issue. Conducting warming-up tests on a large set of candidate services for QoS evaluation is time consuming and expensive, making it vital to implement accurate QoS-estimators. Existing QoS-estimators barely consider the temporal patterns hidden in QoS data. However, such data are naturally time dependent. For addressing this critical issue, this study presents a Kalman-filter-incorporated latent factor analysis (KLFA)-based QoS-estimator for accurate representation to temporally dynamic QoS data. Its main idea is to make the user latent features (LFs) time dependent, while the service ones time consistent. A novel iterative training scheme is designed, where the user LFs are learned through a Kalman filter for precisely modeling the temporal patterns, and the service ones are alternatively trained via an alternating least squares algorithm for precisely representing the historical QoS data. Empirical studies on large-scale and real Web service QoS datasets demonstrate that the proposed KLFA model significantly outperforms state-of-the-art QoS-estimators in estimation accuracy for dynamic QoS data. |
| 资助项目 | National Natural Science Foundation of China[62002337] ; National Natural Science Foundation of China[61873148] ; National Natural Science Foundation of China[62176070] ; National Natural Science Foundation of China[61933007] ; National Natural Science Foundation of China[CAAIXSJLJJ-2021-035A] |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000833058300001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/15991]  |
| 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.Southwest Univ, Coll Comp & Informat Sci, Chongqing 400715, Peoples R China 2.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China 3.Chinese Acad Sci, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 4.Brunel Univ London, Dept Comp Sci, Uxbridge UB8 3PH, Middx, England |
| 推荐引用方式 GB/T 7714 | Yuan, Ye,Luo, Xin,Shang, Mingsheng,et al. A Kalman-Filter-Incorporated Latent Factor Analysis Model for Temporally Dynamic Sparse Data[J]. IEEE TRANSACTIONS ON CYBERNETICS,2022:14. |
| APA | Yuan, Ye,Luo, Xin,Shang, Mingsheng,&Wang, Zidong.(2022).A Kalman-Filter-Incorporated Latent Factor Analysis Model for Temporally Dynamic Sparse Data.IEEE TRANSACTIONS ON CYBERNETICS,14. |
| MLA | Yuan, Ye,et al."A Kalman-Filter-Incorporated Latent Factor Analysis Model for Temporally Dynamic Sparse Data".IEEE TRANSACTIONS ON CYBERNETICS (2022):14. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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