Bayesian Reputation Modeling in E-Marketplaces Sensitive to Subjectivity, Deception and Change

Kevin Regan, Pascal Poupart, Robin Cohen

Abstract: Bayesian Reputation Modeling in E-Marketplaces Sensitive to Subjectivity, Deception and Change Kevin Regan, Pascal Poupart, and Robin Cohen We present a model for buying agents in e-marketplaces to interpret evaluations of sellers provided by other buying agents, known as advisors. The interpretation of seller evaluations is complicated by the inherent subjectivity of each advisor, the possibility that advisors may deliberately provide misleading evaluations to deceive competitors and the dynamic nature of seller and advisor behaviours that may naturally change seller evaluations over time. Using a Bayesian approach, we demonstrate how to cope with subjectivity, deception and change in a principled way. More specifically, by modeling seller properties and advisor evaluation functions as dynamic random variables, buyers can progressively learn a probabilistic model that naturally and “correctly” calibrates the interpretation of seller evaluations without having to resort to heuristics to explicitely detect and filter/discount unreliable seller evaluations. Our model, called BLADE, is shown empirically to achieve lower mean error in the estimation of seller properties when compared to other models for reasoning about advisor ratings of sellers in electronic maketplaces. This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.