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.
Read more from our paper:
Jieming Zhu, Pinjia He, Zibin Zheng, and Michael R. Lyu, "A Privacy-Preserving QoS Prediction Framework for Web Service Recommendation," in Proc. of IEEE International Conference on Web Services (ICWS), 2015. [Paper][Supplemental report][Slides]
The data used in our work is a publicly-released a publicly-available QoS dataset of real-world Web services. The dataset was collected in August 2009, providing a total of 1,974,675 response time and throughput records of service invocations between 339 users and 5,825 Web services. It is available for downloading at:
The source code of our implementations on P-UIPCC and P-PMF has been publicly released. You can view it on our GitHub repository.View code