A Privacy-Preserving QoS Prediction Framework for Web Service Recommendation
Refereed conference paper presented and published in conference proceedings


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 for personalized QoS prediction. These approaches, by leveraging partially observed QoS values from users, can achieve high accuracy of QoS predictions on the unobserved ones. However, the requirement to collect users' QoS data likely puts user privacy at risk, thus making them unwilling to contribute their usage data to a Web service recommender system. As a result, privacy becomes a critical challenge in developing practical Web service recommender systems. In this paper, we make the first attempt to cope with the privacy concerns for Web service recommendation. Specifically, we propose a simple yet effective privacy-preserving framework by applying data obfuscation techniques, and further develop two representative privacy-preserving QoS prediction approaches under this framework. Evaluation results from a publicly-available QoS dataset of real-world Web services demonstrate the feasibility and effectiveness of our privacy-preserving QoS prediction approaches. We believe our work can serve as a good starting point to inspire more research efforts on privacy-preserving Web service recommendation.
著者Zhu J., He P., Zheng Z., Lyu M.R.
會議名稱IEEE International Conference on Web Services, ICWS 2015
會議地點New York
頁次241 - 248
關鍵詞collaborative filtering, privacy preservation, QoS prediction, Web service recommendation

上次更新時間 2021-23-10 於 23:28