Trustable service rating in social networks: a peer prediction method

With the development of social network based applications, different service approaches to achieve these applications have emerged. Users' reporting and sharing of their consumption experience can be utilized to rate the quality of different approaches of online services. How to ensure the authenticity of users' reports and identify malicious ones with cheating reports become important issues to achieve an accurate service rating. In this paper, we provide a private-prior peer prediction mechanism based service rating system with a fusion center, which evaluates users' trustworthiness with their reports by applying the strictly proper scoring rule. In addition, to identify malicious users and bad-functioning/unreliable users with high error rate of quality judgement, an unreliability index is proposed in this paper to evaluate the uncertainty of reports. By combining the trustworthiness and unreliability, malicious users cannot receive a high trustworthiness and low unreliability at the same time when they report falsified feedbacks. Simulation results indicate that the proposed peer prediction based service rating can identify malicious and unreliable users effectively, motivate users to report truthfully, and achieve high service rating accuracy.