RA-QoS: a robust autoencoder-based QoS predictor for highly accurate web service QoS prediction

Abstract

Web services are fundamental for online service-oriented applications, where accurately predicting quality of service (QoS) is critical for recommending optimal services among multiple candidates. Since QoS data often contains noise—stemming from factors like remote user or service locations—current deep neural network (DNN)-based QoS predictors, which generally rely on L2-norm loss functions, face limitations in robustness due to sensitivity to outliers. To address this issue, we propose a novel robust autoencoder-based QoS predictor (RA-QoS) that leverages a hybrid loss function combining bias, training bias, L1-norm and L2-norm to build a robust Autoencoder. This hybrid approach allows RA-QoS to better handle noisy data, minimizing the impact of outliers and biases on prediction accuracy. The RA-QoS model further incorporates preprocessing and training biases, improving its adaptability to real-world QoS data. To evaluate the proposed RA-QoS predictor, extensive experiments are conducted on two real-world QoS datasets. The results demonstrate that our RA-QoS predictor exhibits superior robustness to outliers and higher accuracy in QoS prediction compared to the related state-of-the-art models.

Keywords

• Web service quality
• QoS predicting
• Recommendation systems
• Deep neural networks