Publication

Truthful reputation mechanisms for online systems

Radu Jurca
2007
Thèse EPFL
Résumé

The internet is moving rapidly towards an interactive milieu where online communities and economies gain importance over their traditional counterparts. While this shift creates opportunities and benefits that have already improved our day-to-day life, it also brings a whole new set of problems. For example, the lack of physical interaction that characterizes most electronic transactions, leaves the systems much more susceptible to fraud and deception. Reputation mechanisms offer a novel and effective way of ensuring the necessary level of trust which is essential to the functioning of any market. They collect information about the history (i.e., past transactions) of market participants and make public their reputation. Prospective partners guide their decisions by considering reputation information, and thus make more informative choices. Online reputation mechanisms enjoy huge success. They are present in most e-commerce sites available today, and are seriously taken into consideration by human users. The economical value of online reputation raises questions regarding the trustworthiness of mechanisms themselves. Existing systems were conceived with the assumption that users will share feedback honestly. However, we have recently seen increasing evidence that some users strategically manipulate their reports. This thesis describes ways of making online reputation mechanisms more trustworthy by providing incentives to rational agents for reporting honest feedback. Different kinds of reputation mechanisms are investigated, and for each, I present mechanisms for rewarding the agents that report truthfully. Problems related to collusion (i.e., several agents coordinate their strategies in order to manipulate reputation information) and robustness are also investigated. Moreover, this thesis describes a novel application of incentive compatible reputation mechanisms to the area of quality of service monitoring, and investigates factors that motivate and bias human users when reporting feedback in existing review forums.

À propos de ce résultat
Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.
Concepts associés (34)
Réputation
La réputation est l'opinion (plus techniquement, l'évaluation sociale) du public envers une personne, un groupe, ou une organisation. La réputation est un facteur important dans de nombreux domaines, tels que l'éducation, le commerce, le réseautage social ou le statut social. La réputation est un mécanisme de contrôle social hautement efficace par son ubiquité et sa spontanéité. Elle est un sujet d'étude en sciences sociales, en management, et en technologies des sciences.
Système de réputation
Les systèmes de réputation sont des programmes qui permettent à l'utilisateur d'évaluer d'autres utilisateurs dans les communautés en ligne. Ce réseau d'utilisateurs permet d'apporter de la confiance en un organisme commercial ou un utilisateur au fur et à mesure qu'il gagne en réputation. Ce système est utilisé plus couramment sur des sites de commerce électronique tels qu'Ebay, Amazon.com et Etsy, souvent accompagné d'un système de conseils en ligne, de communautés comme Stack Exchange Network et d'un comité d'arbitrage.
E-réputation
L'e-réputation, parfois appelée web-réputation, cyber-réputation, réputation numérique, sur le Web, sur Internet ou en ligne, est la réputation, l’opinion commune (informations, avis, échanges, commentaires, rumeurs...) sur le Web d'une entité (marque), personne morale (entreprise) ou physique (particulier), réelle (représentée par un nom ou un pseudonyme) ou imaginaire. Elle correspond à l’identité de cette marque ou de cette personne associée à la perception que les internautes s'en font.
Afficher plus
Publications associées (41)

Tackling Peer-to-Peer Discrimination in the Sharing Economy

Boi Faltings, Naman Goel, Maxime Rutagarama

Sharing economy platforms such as Airbnb and Uber face a major challenge in the form of peer-to-peer discrimination based on sensitive personal attributes such as race and gender. As shown by a recent study under controlled settings, reputation systems can ...
ACM2020

Can Who-Edits-What Predict Edit Survival?

Matthias Grossglauser, Lucas Maystre, Victor Kristof

As the number of contributors to online peer-production systems grows, it becomes increasingly important to predict whether the edits that users make will eventually be beneficial to the project. Existing solutions either rely on a user reputation system o ...
ACM2018

AnonRep: Towards Tracking-Resistant Anonymous Reputation

Bryan Alexander Ford, Ennan Zhai

Reputation systems help users evaluate information quality and incentivize civilized behavior, often by tallying feedback from other users such as "likes" or votes and linking these scores to a user’s long-term identity. This identity linkage enables user ...
USENIX2016
Afficher plus