A Reputation Model for Aggregating Ratings based on Beta Distribution Function

Abstract

With the speed growth of financial technology (Fintech), modern electronic marketing has typically deployed the use of the World Wide Web. This has come with great challenges especially in decision making and in engaging the pre-tail for launching new products and services in an open environment susceptible to high risks and threats. A prodigious need to build a sellers reputation and trust between the seller and the buyer so as to diminish such risks and threats in online trading birthed the idea of reputation systems. The emergence of reputation systems has attracted a lot of researchers to propose rating aggregation methods such as simple mean and normal distribution based method. However, the existing methods cannot accurately produce reputation score in some cases. Hence, this paper proposes a new model aiming to producing even more accurate and effective reputation score. Our model uses the standard beta-distribution considering the received rating distribution, so as to generate the weights of each ratings and then derive the level weights of ratings. The final reputation score is the level weighted aggregation of the rating levels. The proposed model is innovative in the aspect that the ratings are not directly aggregated to the reputation score, but are treated as the samples in evaluating each respective rating levels. Through case studies, the model is demonstrated to achieve desired accuracy and effectiveness, and even performs better than the existing models.