RewardRating: A Mechanism Design Approach to Improve Rating Systems

TL;DR

RewardRating is a mechanism that incentivizes honest ratings and discourages fake ones by modeling ratings as investments with rewards, using a game-theoretic approach to improve rating system reliability.

Contribution

The paper introduces RewardRating, a novel mechanism inspired by stock markets, to enhance rating system integrity through incentive design and formal modeling.

Findings

RewardRating effectively discourages fake ratings.

The mechanism aligns user incentives with honest behavior.

Performance analysis shows improved rating accuracy.

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.