Jiminy: A Scalable Incentive-Based Architecture for Improving Rating Quality

  • Evangelos Kotsovinos
  • Petros Zerfos
  • Nischal M. Piratla
  • Niall Cameron
  • Sachin Agarwal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3986)


In this paper we present the design, implementation, and evaluation of Jiminy: a framework for explicitly rewarding users who participate in reputation management systems by submitting ratings. To defend against participants who submit random or malicious ratings in order to accumulate rewards, Jiminy facilitates a probabilistic mechanism to detect dishonesty and halt rewards accordingly.

Jiminy’s reward model and honesty detection algorithm are presented and its cluster-based implementation is described. The proposed framework is evaluated using a large sample of real-world user ratings in order to demonstrate its effectiveness. Jiminy’s performance and scalability are analysed through experimental evaluation. The system is shown to scale linearly with the on-demand addition of slave machines to the Jiminy cluster, allowing it to successfully process large problem spaces.


Trust Management Master Node Reputation System Slave Node Explicit Incentive 
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

  • Evangelos Kotsovinos
    • 1
  • Petros Zerfos
    • 1
  • Nischal M. Piratla
    • 1
  • Niall Cameron
    • 2
  • Sachin Agarwal
    • 1
  1. 1.Deutsche Telekom LaboratoriesBerlinGermany
  2. 2.Pembroke CollegeCambridgeUK

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