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Dealing with Recommendations in a Statistical Trust Model

We previously developed a trust model in which an entity makes decisions in online interactions by using past behavior as a predictor of likely future behavior. However, that approach does not combine recommendations from different entities. This paper focuses on the problem of recommendation combination and detection of unfair recommendations. Our approach involves data analysis methods (Bayesian estimation, Dirichlet distribution), and machine learning methods (Weighted Majority Algorithm). We describe simulation experiments to illustrate the effectiveness and robustness of the methods and the resulting evolution of trust*.