Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies

This paper studies a novel variant of the online fair division problem involving multiple agents in which a learner sequentially observes indivisible items that must be irrevocably allocated to agents while satisfying a desired balance between fairness and efficiency. Existing algorithms assume a small number of items with a sufficiently large number of copies, which ensures an accurate estimation of utilities for all item-agent pairs from noisy utilities. However, this assumption may not hold in many real-life applications, for example, an online platform that has a large number of users (items) who use the platform's service providers (agents) only a few times (a few copies of items), which makes it difficult to accurately estimate utilities for all item-agent pairs. To address this limitation, we assume utility is an unknown function of item-agent features. Building on this assumption, we propose algorithms that model online fair division as a contextual bandit problem, with provable sub-linear regret upper bound guarantees. Our experimental results further validate the effectiveness of the proposed algorithms.