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Robust Reputations for Peer-to-Peer Marketplaces

  • Jonathan Traupman
  • Robert Wilensky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3986)

Abstract

We have developed a suite of algorithms to address two problems confronting reputation systems for large peer-to-peer markets: data sparseness and inaccurate feedback. 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. To handle sparse data, we propose Bayesian versions of both EM-trust and the well-known Percent Positive Feedback system. Using a marketplace simulator, we demonstrate that these algorithms provide more accurate reputations than standard Percent Positive Feedback.

Keywords

Reputation System Online Auction Market Liquidity Bayesian Algorithm Percent Positive Feedback 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jonathan Traupman
    • 1
  • Robert Wilensky
    • 1
  1. 1.Computer Science DivisionU.C. BerkeleyBerkeleyUSA

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