A Double-Space and Double-Norm Ensembled Latent Factor Model for Highly Accurate Web Service QoS Prediction
Di Wu, Peng Zhang, Yi He, Xin Luo · IEEE Transactions on Services Computing · 2022
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 anL2-norm-orientedLossfunction only. They cannot comprehensively represent the characteristics of target QoS data to make accurate predictions because inner product space andL2-norm have their respective limitations. To address this issue, this study proposes a Double-space and Double-norm Ensembled Latent Factor (D2E-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 anL1-and-L2-norm-orientedLossfunction, and 3) Ensembled—building an ensemble of these two predictors by a weighting strategy. By doing so, D2E-LF integrates multi-merits originating from inner product space, distance space,L1-norm, andL2-norm, making it achieve highly accurate QoS prediction. Experiments on two real-world QoS datasets demonstrate that D2E-LF has significantly higher prediction accuracy than state-of-the-art models.