RewardRating: a mechanism design approach to improve rating systems

Nowadays, rating systems play a crucial role in the attraction of customers to different services. However, as it is difficult to detect a fake rating, fraudulent users can potentially unfairly impact the rating's aggregated score. This fraudulent behavior can negatively affect customers and businesses. To improve rating systems, in this paper, we take a novel mechanism-design approach to increase the cost of fake ratings while providing incentives for honest ratings. However, designing such a mechanism is a challenging task, as it is not possible to detect fake ratings since raters might rate a same service differently. Our proposed mechanism RewardRating is inspired by the stock market model in which users can invest in their ratings for services and receive a reward on the basis of future ratings. We leverage the fact that, if a service's rating is affected by a fake rating, then the aggregated rating is biased toward the direction of the fake rating. First, we formally model the problem and discuss budget-balanced and incentive-compatibility specifications. Then, we suggest a profit-sharing scheme to cover the rating system's requirements. Lastly, we analyze the performance of our proposed mechanism.