Hidden and Indirect (Probabilistically Estimated) Reputations - Hiper Method

Open access

Abstract

It is a challenge to design a well balanced reputation system for an environment with millions of users. A reputation system must also represent user reputation as a value which is simple and easy to compare and will give users straightforward suggestions who to trust. Since reputation systems rely on feedbacks given by users, it is necessary to collect unbiased feedbacks

In this paper we present a controversial, yet innovative reputation system. Hidden and Indirect (Probabilistically Estimated) Reputations - HIPER Method splits user reputation into two related values: Hidden Reputation (HR) is directly calculated from a set of feedbacks, Indirect Reputation (IR) is a probabilistically estimated projection of the hidden reputation and its value is public. Such indirect connection between received feedbacks and a visible reputation value allows users to provide unbiased feedbacks without fear of retaliation.

References

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Foundations of Computing and Decision Sciences

The Journal of Poznan University of Technology

Journal Information


CiteScore 2016: 0.75

SCImago Journal Rank (SJR) 2016: 0.330
Source Normalized Impact per Paper (SNIP) 2016: 0.709

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