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Journal Articles Autonomous Agents and Multi-Agent Systems Year : 2020

Personalised rating

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 bribery-proof, 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 NP-complete, even with full knowledge of the underlying network structure.
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Dates and versions

hal-03066902 , version 1 (15-12-2020)

Identifiers

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Umberto Grandi, James Stewart, Paolo Turrini. Personalised rating. Autonomous Agents and Multi-Agent Systems, 2020, 34 (article 55), pp.1-42. ⟨10.1007/s10458-020-09479-2⟩. ⟨hal-03066902⟩
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