Bandit Learning in Many-to-One Matching Markets

The problem of two-sided matching markets is well-studied in social science and economics. Some recent works study how to match while learning the unknown preferences of agents in one-to-one matching markets. However, in many cases like the online recruitment platform for short-term workers, a company can select more than one agent while an agent can only select one company at a time. These short-term workers try many times in different companies to find the most suitable jobs for them. Thuswe consider a more general bandit learning problem in many-to-one matching markets where each arm has a fixed capacity and agents make choices with multiple rounds of iterations. We develop algorithms in both centralized and decentralized settings and prove regret bounds of order O(log T) and O(log(2)T) respectively. Extensive experiments show the convergence and effectiveness of our algorithms.