@Article{yu02b,
  author =	 {Bin Yu and Munindar P. Singh},
  title =	 {Distributed Reputation Management for Electronic
                  Commerce},
  googleid = 	 {tosakQiNkp8J:scholar.google.com/},
  journal =	 {Computational Intelligence},
  year =	 2002,
  volume =	 18,
  number =	 4,
  pages =	 {535--549},
  abstract =	 {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.},
  keywords =     {multiagent trust ecommerce},
  url =		 {http://jmvidal.cse.sc.edu/library/yu02b.pdf},
  cluster = 	 {11498407866563988406}
}