推荐系统和自我激励用户
Recommendation Systems and Self Motivated Users
摘要：
现代推荐系统依赖于群体的智慧来学习最佳的行动方案。这导致了系统学习（探索）的目标与个体用户采取同期最佳行动（利用）的目标之间的内在激励错位。这类系统的设计必须考虑到这一点，并考虑到用户可获得的其他信息。一个突出但简单的例子是，当代理顺序到达时，每个代理都观察他的前任的行动和奖励。我们提供了一个激励兼容和渐近最优的机制，该设置。该机制的复杂性表明，为一般环境设计这种系统是一项具有挑战性的任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
