Personalised rating
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(2020) 34:55
Personalised rating Umberto Grandi1 · James Stewart2 · Paolo Turrini3
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract We introduce personalised rating, a network-based rating system where individuals, connected in a social network, decide whether or not to consume a service (e.g., a restaurant) based on the evaluations provided by their peers. We compare personalised rating with the more widely used objective rating where, instead, customers receive an aggregate evaluation of what everybody else has declared so far. We focus on the manipulability of such systems, allowing a malicious service provider (e.g., the restaurant owner) to transfer monetary incentive to the individuals in order to manipulate their rating and increase the overall profit. We study manipulation under various constraints, such as the proportion of individuals who evaluate the service and, in particular, how much the attacker knows of the underlying customers’ network, showing the conditions under which the system is briberyproof, i.e., no manipulation strategy yields a strictly positive expected gain to the service provider. We also look at manipulation strategies that are feasible in theory but might, in general, be infeasible in practice, deriving a number of algorithmic properties of manipulation under personalised rating. In particular we show that establishing the existence of a rewarding manipulation strategy for the attacker—and, notably, an optimal one—is NPcomplete, even with full knowledge of the underlying network structure. Keywords Trust and reputation · Social networks · Bribery and strategic behaviour · Computational social choice This work revises and extends papers presented at IJCAI-2016 [28] and at AAAI-2018 [29]. We are grateful for the feedback received by several anonymous reviewers as well as the audiences of COMSOC-2018 in Troy, USA, the Workshop on Theoretical Aspects of e-Democracy in Toulouse, France, in 2017, and the 6th World Congress of the Game Theory Society in Maastricht, The Netherlands, in 2016. * Umberto Grandi [email protected] James Stewart [email protected] Paolo Turrini [email protected] 1
Institut de Recherche en Informatique de Toulouse (IRIT), University of Toulouse, Toulouse, France
2
Department of Computer Science, University of Oxford, Oxford, UK
3
Department of Computer Science, University of Warwick, Coventry, UK
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Autonomous Agents and Multi-Agent Systems
(2020) 34:55
1 Introduction We use online reviews all the time: for food, movies and even doctors. But can we trust them? Online rating systems such as TripAdvisor, Amazon or Netflix, where a small proportion of users writes reviews which are read by a potentially large number of others, are clearly manipulable: each service provider is able to offer a compensation—monetary or otherwise—in exchange for a positive review, having an impact on the whole set of potential customers. These systems are based on what we call objectiv
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