  Nowadays, rating systems play a crucial role in the attraction of customers
for different services. However, as it is difficult to detect a fake rating,
attackers can potentially impact the rating's aggregated score unfairly. This
malicious behavior can negatively affect users and businesses. To overcome this
problem, we take a mechanism-design approach to increase the cost of fake
ratings while providing incentives for honest ratings. Our proposed mechanism
\textit{RewardRating} is inspired by the stock market model in which users can
invest in their ratings for services and receive a reward based on future
ratings. 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. Finally, we analyze the
performance of our proposed mechanism.
