A Clustering-Based User Reputation Evaluation Approach for Web Service Recommendation

In web service recommender systems, users are always asked to provide their observed QoS data to assist the personalized QoS prediction for other users. Most existed approaches assume that all the users will provide real data to the system, however the dishonest users may be appeared in many recommender systems. Attracted by commercial benefit, some users may intentionally provide unfair feedback inconsistent with their real experience, which will harm to the robustness of service recommender system. In this paper, we propose a clustering-based reputation evaluation approach to identify the dishonest users. Firstly, we calculate the trustworthy cluster on each service by Clustering of users' QoS feedback. Then the feedback of users will be classified according to their deviation degree from the trustworthy cluster. Finally, according to the users' statistic feedback information, we apply Beta reputation model to evaluate users' reputation dynamically. Experimental results demonstrate that this approach can accurately evaluate users' reputaion compared to other state-of-the-art approaches.