  Multiple machine learning and prediction models are often used for the same
prediction or recommendation task. In our recent work, where we develop and
deploy airline ancillary pricing models in an online setting, we found that
among multiple pricing models developed, no one model clearly dominates other
models for all incoming customer requests. Thus, as algorithm designers, we
face an exploration - exploitation dilemma. In this work, we introduce an
adaptive meta-decision framework that uses Thompson sampling, a popular
multi-armed bandit solution method, to route customer requests to various
pricing models based on their online performance. We show that this adaptive
approach outperform a uniformly random selection policy by improving the
expected revenue per offer by 43% and conversion score by 58% in an offline
simulation.
