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CSpace
A Double-Space and Double-Norm Ensembled Latent Factor Model for Highly Accurate Web Service QoS Prediction
Wu, Di1,2; Zhang, Peng3; He, Yi4; Luo, Xin1,2
2023-03-01
摘要Quality-of-Service (QoS), which describes the non-functional characteristics of Web service, is of great significance in service selection. Since users cannot invoke all services to obtain the corresponding QoS data, QoS prediction becomes a hot yet thorny issue. To date, a latent factor analysis (LFA)-based QoS predictor is one of the most successful and popular approaches to address this issue. However, current LFA-based QoS predictors are mostly modeled on inner product space with an L-2-norm-oriented Loss function only. They cannot comprehensively represent the characteristics of target QoS data to make accurate predictions because inner product space and L-2-norm have their respective limitations. To address this issue, this study proposes a Double-space and Double-norm Ensembled Latent Factor ((DE)-E-2-LF) model. Its main idea is three-fold: 1) Double-space-inner product space and distance space are employed to model two kinds of LFA-based QoS predictors, respectively, 2) Double-norm-both of these two predictors adopt an L-1-and-L-2-norm-oriented Loss function, and 3) Ensembled-building an ensemble of these two predictors by a weighting strategy. By doing so, (DE)-E-2-LF integrates multi-merits originating from inner product space, distance space, L-1-norm, and L-2-norm, making it achieve highly accurate QoS prediction. Experiments on two real-world QoS datasets demonstrate that (DE)-E-2-LF has significantly higher prediction accuracy than state-of-the-art models.
关键词Web service service selection Quality-of-Service (QoS) latent factor analysis missing data prediction big data
DOI10.1109/TSC.2022.3178543
发表期刊IEEE TRANSACTIONS ON SERVICES COMPUTING
ISSN1939-1374
卷号16期号:2页码:802-814
通讯作者Luo, Xin(luoxin21@gmail.com)
收录类别SCI
WOS记录号WOS:000965129700003
语种英语