Unfair Web Service QoS Feedback Filtering for Service Recommendation

In order to recommend the best web service to users, the QoS feedback information provided by advisors are always needed to predict the QoS of candidate services. Attracted by commercial benefit, some advisors may intentionally provide unfair feedback inconsistent with their real experience, which will cause the deviation of prediction results. To attack this problem, an unfair QoS feedback filtering algorithm based on beta reputation model is proposed in this paper. Firstly, several certified center users are obtained to initialize the trustworthy users set. Further, we evaluate the deviation between the target user's feedback and the average value of trustworthy users set. Finally, each user's reputation is calculated based on how many deviated feedback he has submitted. The users whose reputation exceed the trustworthy threshold will be recognized as trustworthy users. After several iterations, a majority of unfair users are filtered out. Experimental results demonstrate that our approach can accurately filter out the unfair users and improve the robustness of QoS prediction algorithms against unfair feedback attacks.