A Randomized Reputation System in the Presence of Unfair Ratings

Abstract

With the increasing popularity of online shopping markets, a significant number of consumers rely on these venues to meet their demands while choosing different products based on the ratings provided by others. Simultaneously, consumers feel confident in expressing their opinions through ratings. As a result, millions of ratings are generated on the web for different products, services, and dealers. Nonetheless, a noticeable number of users post unfair feedback. Recent studies have shown that reputation escalation is emerging as a new service, by which dealers pay to receive good feedback and escalate their ratings in online shopping markets. Therefore, finding robust and reliable ways to distinguish between fake and trustworthy ratings from users is a crucial task for every online shopping market. Moreover, with the dramatic increase in the number of ratings provided by consumers, scalability has arisen as another significant issue in the existing methods of reputation systems. To tackle these issues, we propose a randomized algorithm that calculates the reputation based on a random sample of the ratings. Since the randomly selected sample has a logarithmic size, it guarantees feasible scalability for large-scale online review systems. In addition, the randomness nature of the algorithm makes it robust against unfair ratings. We provide a thorough theoretical analysis of the proposed algorithm and validate its effectiveness through extensive empirical evaluation using real-world and synthetically generated datasets. Our experimental results show that the proposed method provides a high accuracy while running much faster than the existing iterative filtering approaches.