Trust-aware privacy-preserving QoS prediction with graph neural collaborative filtering for internet of things services

The booming development of the Internet of Things (IoT) has led to an explosion of web services, making it more inconvenient for users to choose satisfactory services among numerous options. Therefore, ensuring quality of service (QoS) in a service-oriented IoT environment is crucial, highlighting QoS prediction as a prominent research focus. However, issues related to information credibility, user data privacy, and prediction accuracy in QoS prediction for IoT services have become significant challenges in current research. To tackle these issues, we propose TPP-GNCF, a trust-aware privacy-preserving QoS prediction framework that integrates graph neural networks with collaborative filtering methods. In TPP-GNCF, we filter out untrustworthy QoS values provided by users for certain services to select credible QoS values. Then, a message-passing graph neural network (MP-GNN) is utilized to effectively capture information transmission and relationships in the graph structure, while differential privacy is used to protect user node information. In addition, we use a similarity calculation method based on weight function in collaborative filtering to mine implicit embedded features that graph neural networks cannot directly utilize. Finally, the final missing QoS values are achieved by fusing graph neural predicted QoS and feature collaborative filtering predicted QoS. We conducted extensive experiments on the well-known WS-DREAM dataset. The results demonstrate that the TPP-GNCF framework not only surpasses existing schemes in performance but also effectively addresses issues of information credibility and user privacy.