An Online Personalized Reputation Estimation Model for Service-Oriented Systems

In service-oriented computing environments, many Web services are provided for users to build service-oriented systems. Since the performance of the same Web service is different from different users' perspectives, users have to personally select the optimal Web services according to quality-of-service(QoS) data observed by other similar users. However, users with low reputations will provide unreliable data, which will have a negative impact on service selection. Moreover, the QoS data vary over time due to changes in user reputation. Therefore, how to estimate a personalized reputation for each user at runtime remains a significant problem. To address this critical challenge, this paper proposes an online reputation estimation method, called OPRE, to efficiently provide a personalized reputation for each user. Based on the users' observed QoS data, OPRE employs matrix factorization and online learning techniques to estimate personalized reputations. The experimental results show that OPRE has high effectiveness compared to other approaches.