  The problem of two-sided matching markets has a wide range of real-world
applications and has been extensively studied in the literature. A line of
recent works have focused on the problem setting where the preferences of
one-side market participants are unknown \emph{a priori} and are learned by
iteratively interacting with the other side of participants. All these works
are based on explore-then-commit (ETC) and upper confidence bound (UCB)
algorithms, two common strategies in multi-armed bandits (MAB). Thompson
sampling (TS) is another popular approach, which attracts lots of attention due
to its easier implementation and better empirical performances. In many
problems, even when UCB and ETC-type algorithms have already been analyzed,
researchers are still trying to study TS for its benefits. However, the
convergence analysis of TS is much more challenging and remains open in many
problem settings. In this paper, we provide the first regret analysis for TS in
the new setting of iterative matching markets. Extensive experiments
demonstrate the practical advantages of the TS-type algorithm over the ETC and
UCB-type baselines.
