Contextual bandit algorithms have become widely used for recommendation in
online systems (e.g. marketplaces, music streaming, news), where they now wield
substantial influence on which items get exposed to the users. This raises
questions of fairness to the items -- and to the sellers, artists, and writers
that benefit from this exposure. We argue that the conventional bandit
formulation can lead to an undesirable and unfair winner-takes-all allocation
of exposure. To remedy this problem, we propose a new bandit objective that
guarantees merit-based fairness of exposure to the items while optimizing
utility to the users. We formulate fairness regret and reward regret in this
setting, and present algorithms for both stochastic multi-armed bandits and
stochastic linear bandits. We prove that the algorithms achieve sub-linear
fairness regret and reward regret. Beyond the theoretical analysis, we also
provide empirical evidence that these algorithms can fairly allocate exposure
to different arms effectively.

为了解决个性化推荐中传统策略可能导致的不公平现象，提出了一种新的基于功绩公平的策略，并给出了两种算法，分别适用于多臂老虎机和线性老虎机，证明了算法的功绩公平和奖励公平具有亚线性，同时提供了实证分析，证明了该算法能够有效地公平分配推荐曝光。