Matching While Learning
作者研究了平台在有限资源下如何应对新用户的冷启动问题。当新用户到来时，平台需要学习其属性（探索）以便未来更好匹配（利用），但推荐物品数量有限。例如，劳动力市场平台在职位有限时如何匹配工人与工作。作者将每个工人视为一个多臂老虎机问题，并通过不同职位类型的供应约束将它们耦合。作者提出一个解决方案：平台应为每种职位类型估算一个影子价格，并根据这些价格调整每个工人的收益，早期用于平衡学习与收益，之后则进行短视匹配。
Platforms face a cold start problem whenever new users arrive: namely, the platform must learn attributes of new users (explore) in order to match them better in the future (exploit). How should a platform handle cold starts when there are limited quantities of the items being recommended? For instance, how should a labor market platform match workers to jobs over the lifetime of the worker, given a limited supply of jobs? In this setting, there is one multiarmed bandit problem for each worker, coupled together by the constrained supply of jobs of different types. A solution is developed to this problem. It is found that the platform should estimate a shadow price for each job type, and for each worker, adjust payoffs by these prices (i) to balance learning with payoffs early on and (ii) to myopically match them thereafter.