Using Graph Neural Network to Enhance Quality of Service Prediction

Abstract

Quality of service (QoS) prediction has great importance in today's web services computing. Several researchers proposed methods to enhance the quality of service prediction. The most used one is collaborative filtering (CF), which can be categorized into three main categories: memory-based algorithms, model-based algorithms, and context-based CF algorithms. This paper proposes a model-based algorithm using the graph neural network (GNN) to predict the QoS values. To evaluate the performance of the proposed method, an experiment was conducted. The WS-dream dataset used in the experiment and the proposed method performance were compared with three baseline methods (User item-based Pearson correlation coefficient for QoS prediction-UIPCC, reputation-aware network embedding-based QoS Prediction-RANEP, and trust-aware approach TAP for personalized QoS prediction). The experiment results show that the proposed method, the GNN-based QoS prediction algorithm, performs better than memory-based and other model-based methods in terms of RMSE and MAE in most cases.