Recommendation systems often face exploration-exploitation tradeoffs: the
system can only learn about the desirability of new options by recommending
them to some user. Such systems can thus be modeled as multi-armed bandit
settings; however, users are self-interested and cannot be made to follow
recommendations. We ask whether exploration can nevertheless be performed in a
way that scrupulously respects agents' interests---i.e., by a system that acts
as a fiduciary. More formally, we introduce a model in which a recommendation
system faces an exploration-exploitation tradeoff under the constraint that it
can never recommend any action that it knows yields lower reward in expectation
than an agent would achieve if it acted alone. Our main contribution is a
positive result: an asymptotically optimal, incentive compatible, and ex-ante
individually rational recommendation algorithm.

本文介绍了一种新的推荐系统模型，其面临探索 - 开发折衷，并可以被建模为多臂老虎机设置，由于用户具有自利性质，因此不能被强制性遵循推荐，作者提出了一种算法以探索为基础，同时考虑到代理人的利益，是一个渐进最优、刺激兼容、先验个体理性的推荐算法。