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.

我们引入了个性化评分，这是一个基于网络的评分系统，在该系统中，连接到社交网络中的个人根据其同龄人提供的评估来决定是否消费服务（例如，餐厅）。我们将个性化评级与更广泛使用的客观评级进行比较，在该评级中，客户获得了到目前为止其他人已经宣布的内容的综合评估。我们专注于此类系统的可操作性，允许恶意服务提供商（例如，餐馆老板）将金钱激励转移给个人，以操纵他们的评分并增加整体利润。我们研究各种约束条件下的操作，例如评估服务的个人比例，尤其是攻击者对底层客户网络的了解程度，展示了系统防贿赂的条件，即没有任何操纵策略会给服务提供商带来严格的积极预期收益。我们还将研究理论上可行但通常在实践中不可行的操纵策略，从而得出个性化评级下操纵的多种算法特性。特别是，我们表明，建立针对攻击者的奖励操纵策略（尤其是最佳策略）的存在是NP-完整，即使完全了解底层网络结构也是如此。