A Privacy-Preserving QoS Prediction Framework for Web Service Recommendation

摘要: QoS-based Web service recommendation has recently gained much attention for providing a promising way to help users find high-quality services. To facilitate such recommendations, existing studies suggest the use of collaborative filtering techniques personalized QoS prediction. These approaches, by leveraging partially observed values from users, can achieve high accuracy predictions on unobserved ones. However, requirement collect users' data likely puts user privacy at risk, thus making them unwilling contribute their usage recommender system. As result, becomes critical challenge in developing practical systems. In this paper, we make first attempt cope with concerns recommendation. Specifically, propose simple yet effective privacy-preserving framework applying obfuscation techniques, and further develop two representative prediction approaches under framework. Evaluation results publicly-available dataset real-world services demonstrate feasibility effectiveness our approaches. We believe work serve as good starting point inspire more research efforts