A probabilistic reputation model

This work introduces a probabilistic model of reputation. It is based on the following simple consumer-provider interaction model. Consumers are assumed to order items to providers, who each have some internal, latent, “quality of service” score. In the basic model, the providers supply the items with a quality following a normal law, centered on their internal “quality of service”. The consumers, after the reception of the item, rate it according to a linear function of its quality (a standard regression model). This regression model accounts for the bias of the consumer in providing ratings as well as his reactivity towards changes in item quality. Moreover, the constancy of the provider in supplying an equal quality level for delivering the items is estimated by the standard deviation of his normal law of item quality generation. Symmetrically, the consistancy of the consumer in providing similar ratings for a given quality is quantified by the standard deviation of his normal law of ratings generation. Two extensions of this basic model are considered as well: a model accounting for truncation of the ratings and a Bayesian model assuming a prior distribution on the parameters. Expectation-maximization algorithms allowing to provide estimations of the parameters only based on the ratings are developed for all the models. The experiments suggest that these models are able to extract useful information from the ratings.