  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 system's 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.
