QoS Prediction for Web Services via Combining Multi-component Graph Convolutional Collaborative Filtering and Deep Factorization Machine

Abstract

QoS prediction for Web Services is becoming increasingly important for various QoS-aware Web Services management tasks. However, the existing methods for QoS prediction of Web Services have some drawbacks, such as poor performance in dealing with data sparsity, insufficient consideration of latent information in user-service interaction behavior, and no consideration on discriminating the weight of latent information. To address these shortcomings, this paper proposes a QoS Prediction approach via combining multi-component graph convolutional collaborative filtering and deep factorization machine. A user-service bipartite graph is constructed, and the edges of the graph are decomposed into multiple latent spaces with node-level attention to identify latent components. Then, the importances of latent components are determined, and they are aggregated to obtain the corresponding user-service embedding vectors. Finally, the embedding vectors are taken as the input of a deep factorization machines model to obtain the prediction of unknown QoS. Extensive experiments are conducted on a real-world dataset. The experimental results demonstrate that MGCCF-DFM achieves superior prediction accuracy in terms of mean absolute error (MAE) and root mean square error (RMSE) compared with the existing QoS prediction techniques.