Using Jiminy for Run-Time User Classification Based on Rating Behaviour

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.