Jiminy: A Scalable Incentive-Based Architecture for Improving Rating Quality

In this paper we present the design, implementation, and evaluation of Jiminy: a framework for explicitly rewarding users who participate in reputation management systems by submitting ratings. To defend against participants who submit random or malicious ratings in order to accumulate rewards, Jiminy facilitates a probabilistic mechanism to detect dishonesty and halt rewards accordingly. Jiminy’s reward model and honesty detection algorithm are presented and its cluster-based implementation is described. The proposed framework is evaluated using a large sample of real-world user ratings in order to demonstrate its effectiveness. Jiminy’s performance and scalability are analysed through experimental evaluation. The system is shown to scale linearly with the on-demand addition of slave machines to the Jiminy cluster, allowing it to successfully process large problem spaces.