Optimizing advisor network size in a personalized trust-modelling framework for multi-agent systems

Abstract

Zhang [1] has recently proposed a novel trust-based framework for systems including electronic commerce. This system relies on a model of the trustworthiness of advisors (other buyers offering advice to the current buyer) which incorporates estimates of each advisor's private and public reputations. Users create a social network of trusted advisors, and sellers will offer better rewards to satisfy trusted advisors and thus build their own reputations. © 2010 Springer-Verlag Berlin Heidelberg.