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This paper describes an application of our prototype implementation of Jiminy, a scalable distributed architecture for providing participation incentives in online rating schemes. Jiminy is based on an incentive model where participants are explicitly rewarded for submitting ratings, and are debited when they query a participating reputation management system (RMS). Providing explicit incentives increases the quantity of ratings submitted and reduces their bias by removing implicit or hidden rewards, such as those gained through revenge or reciprocal ratings. To prevent participants from submitting arbitrary or dishonest feedback for the purpose of accumulating rewards, Jiminy halts rewards for participants who are deemed dishonest by its probabilistic honesty estimator. Using this estimator, Jiminy can also perform classification of users based on their rating behaviour, which can be further used as criteria for filtering the rating information that users obtain from the RMS. More background on the theoretical foundations of Jiminy can be found in [1], while [2] provides details on the system design, implementation and performance evaluation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]