A Modified Bayesian Trustworthiness Evaluation Method to Mitigate the Effect of Unfair Ratings

The choice of trustworthy interaction partners is one of the key factors for successful transactions in online communities. To choose the most trustworthy sellers to interact with, buyers rely on trust and reputation models. Therefore, online systems must be able to accurately assess peers’ trustworthiness. The Beta distribution function provides a sound mathematical basis for combining feedback and deriving users’ trustworthiness. But the Beta reputation system suffers from many forms of cheating behavior such as the proliferation of unfair positive ratings, leading a poor service provider to build a good reputation, and the proliferation of unfair negative feedback, leading a good service provider to end up with a bad reputation. In this paper, we propose a new and coherent method for computing users’ trustworthiness by combining the Beta trustworthiness expectation function with the credibility function. This novel combination mechanism mitigates the impact of unfair ratings. In comparison with Bayesian trust model, we quantitatively show that our approach provides significantly more accurate estimation of peers’ trustworthiness through feedback gathered from multiple sources. Furthermore, we propose an extension of Bayesian trustworthiness expectation function by introducing the initial trust propensity to allow assessing individuals’ initial trust.