Recommendation systems are dynamic economic systems that balance the needs of
multiple stakeholders. A recent line of work studies incentives from the
content providers' point of view. Content providers, e.g., vloggers and
bloggers, contribute fresh content and rely on user engagement to create
revenue and finance their operations. In this work, we propose a contextual
multi-armed bandit setting to model the dependency of content providers on
exposure. In our model, the system receives a user context in every round and
has to select one of the arms. Every arm is a content provider who must receive
a minimum number of pulls every fixed time period (e.g., a month) to remain
viable in later rounds; otherwise, the arm departs and is no longer available.
The system aims to maximize the users' (content consumers) welfare. To that
end, it should learn which arms are vital and ensure they remain viable by
subsidizing arm pulls if needed. We develop algorithms with sub-linear regret,
as well as a lower bound that demonstrates that our algorithms are optimal up
to logarithmic factors.

本文提出了一种上下文多臂赌博机模型来解决推荐系统中内容提供商对曝光量的依赖性，并开发具有次线性遗憾度和优化目标的算法，该算法的目标是最大程度地提高用户的福利和保留至关重要的内容提供商。