<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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</p><p>In this paper we present a controversial, yet innovative reputation system. <italic>Hidden and Indirect (Probabilistically Estimated) Reputations - HIPER Method</italic> splits user reputation into two related values: <italic>Hidden Reputation (HR)</italic> is directly calculated from a set of feedbacks, <italic>Indirect Reputation (IR)</italic> 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.</p></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 feedbacksIn 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.

Hidden and Indirect (Probabilistically Estimated) Reputations - Hiper Method

Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Hidden and Indirect (Probabilistically Estimated) Reputations - Hiper Method"}

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...

Hidden and Indirect (Probabilistically Estimated) Reputations - Hiper Method

Publié en ligne: 01 mars 2015

Pages: 35 - 51

DOI: https://doi.org/10.1515/fcds-2015-0003

Mots clés
trust management method, reputation system, e-commerce, online auctions

© 2015 Konrad Leszczyński and Maciej Zakrzewicz

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Hidden and Indirect (Probabilistically Estimated) Reputations - Hiper Method

Publié en ligne: 01 mars 2015

Pages: 35 - 51

DOI: https://doi.org/10.1515/fcds-2015-0003

Mots cléstrust management method, reputation system, e-commerce, online auctions

© 2015 Konrad Leszczyński and Maciej Zakrzewicz

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
trust management method, reputation system, e-commerce, online auctions