Working draft submitted to CHI 2006 Robust Reputations for Peer-to-peer Marketplaces ∗

Abstract

We have developed a suite of algorithms to address two large problems confronting reputation systems for large peer-topeer markets: data sparseness and inaccurate feedback. To handle sparse data, we propose a Bayesian version of the well-known Percent Positive Feedback system. To mitigate the effect of inaccurate feedback – particularly retaliatory negative feedback – we propose EM-trust, which uses a latent variable statistical model of the feedback process. Using a marketplace simulator, we demonstrate that both of these algorithms provide more accurate reputations than standard Percent Positive Feedback. Finally, we show that even better performance can be obtained by combining the two approaches into a Bayesian EM-trust reputation system