We study regret minimization problems in a two-sided matching market where uniformly valued demand side agents (a.k.a. agents) continuously compete for getting matched with supply side agents (a.k.a. arms)with unknown and heterogeneous valuations. Such markets abstract online matching platforms ( for e.g. UpWork, TaskRabbit) and falls within the purview of matching bandit models introduced in Liu et al.
<clarity> We study regret minimization problems in a two-sided matching market where uniformly valued demand side agents (a.k.a. agents) continuously compete for getting matched with supply side agents (a.k.a. arms)with unknown and heterogeneous valuations. Such markets abstract online matching platforms ( for e.g. UpWork, TaskRabbit) and falls within the purview of matching bandit models introduced in Liu et al.
We study regret minimization problems in a two-sided matching market where uniformly valued demand side agents (a.k.a. agents) continuously compete for getting matched with supply side (arms), abstracts the complex interactions under partial information on matching platforms ( for e.g. UpWork, TaskRabbit) and falls within the purview of matching bandit models introduced in Liu et al.