A Double-Space and Double-Norm Ensembled Latent Factor Model for Highly Accurate Web Service QoS Prediction

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 (D
²
E-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, D
²
E-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 D
²
E-LF has significantly higher prediction accuracy than state-of-the-art models.