This paper considers a novel online fair division problem involving multiple agents in which a learner observes an indivisible item that has to be irrevocably allocated to one of the agents while satisfying a fairness and efficiency constraint. Existing algorithms assume a small number of items with a sufficiently large number of copies, which ensures a good utility estimation for all item-agent pairs. However, such an assumption may not hold in many real-life applications, e.g., an online platform that has a large number of users (items) who only use the platform's service providers (agents) a few times (a few copies of items), which makes it difficult to estimate the utility for all item-agent pairs. To overcome this challenge, we model the online fair division problem using contextual bandits, assuming the utility is an unknown function of the item-agent features. We then propose algorithms for online fair division with sub-linear regret guarantees. Our experimental results also verify the different performance aspects of the proposed algorithms.

本研究针对在线公平分配中的多个代理人问题，旨在解决现有算法在用户数量庞大且每个用户仅使用少量服务时所面临的效用估计困难。通过引入情境乐队模型，本文提出了保证亚线性遗憾的在线公平分配算法，实验结果验证了所提算法在不同性能方面的优势。

基于情境乐队的在线公平分配