RewardRating: A Mechanism Design Approach to Improve Rating Systems

Abstract

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.