Modern recommendation systems rely on the wisdom of the crowd to learn the optimal course of action. This induces an inherent mis-alignment of incentives between the systems objective to learn (explore) and the individual users objective to take the contemporaneous optimal action (exploit). The design of such systems must account for this and also for additional information available to the users. A prominent, yet simple, example is when agents arrive sequentially and each agent observes the action and reward of his predecessor. We provide an incentive compatible and asymptotically optimal mechanism for that setting. The complexity of the mechanism suggests that the design of such systems for general settings is a challenging task.
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