One of the major challenges for electronic commerce is how to establish a relationship of trust between different parties. Establishing trust is nontrivial, because the traditional physical or social means of trust cannot apply directly in virtual settings. In many cases, the parties involved may not ever have interacted before. Reputation systems seek to address the development of trust by recording the reputations of different parties. However, most existing reputation systems are restricted to individual market web-sites. Further, relevant information about a party may come from several web-sites and from interaction that were not mediated by any web-site. This paper considers the problem of automatically collecting ratings about a given party from others. Our approach involves a distributed agent architecture and adapts the mathematical theory of evidence to represent and propagate the ratings that participants give to each other. When evaluating the trustworthiness of a given party, a peer combines its local evidence (based on direct prior interactions with the party) with the testimonies of others regarding the same party. This approach satisfies certain important properties of distributed reputation management and is experimentally evaluated through simulations.
In REF , a distributed personalized reputation management approach for e-commerce is proposed in which ideas from mathematical theory of evidence were adopted to represent and evaluate reputation.
Distributed Reputation Management for Electronic Commerce
Computational Intelligence
Computer Science