中文版 | English
CSpace
A Kalman-Filter-Incorporated Latent Factor Analysis Model for Temporally Dynamic Sparse Data
Yuan, Ye1; Luo, Xin1; Shang, Mingsheng2,3; Wang, Zidong4
2022-07-25
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.
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
DOI10.1109/TCYB.2022.3185117
发表期刊IEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
页码14
通讯作者Luo, Xin(luoxin21@gmail.com)
收录类别SCI
WOS记录号WOS:000833058300001
语种英语