A Double-Norm Aggregated Latent Factorization of Tensors Model for Temporal-Aware QoS Prediction

Time-varying Quality-of-Services (QoS) data describes the non-functional characteristics of Web service, which plays a key role in service selection. Whereas, QoS data is often high-dimensional and incomplete (HDI) due to the impossibility for users to request all services. A Latent Factorization of Tensors (LFT)-based QoS predictor proves to be efficient in predicting time-varying QoS data. However, current LFT models mostly use
$L_{2}$
-norm-oriented Loss. Yet
$L_{2}$
norm is sensitive to outlier data, resulting model robustness not being guaranteed. Moreover, although
$L$
, norm has intrinsically robustness, it is less sensitive to error. To address the above problems, this study proposes a Double-norm Aggregated Latent factorization of tensors (DAL) model. Its main idea is to aggregate
$L_{2}$
-norm and smooth
$L_{1}$
-norm to form its Loss, making it have both high accuracy and strong robustness in predicting the unobserved time-varying QoS data. Empirical studies on two time-varying QoS datasets shows that the proposed model has higher prediction accuracy and better convergence rate than state-of-the-art models.