  In this paper we apply active learning algorithms for dynamic pricing in a
prominent e-commerce website. Dynamic pricing involves changing the price of
items on a regular basis, and uses the feedback from the pricing decisions to
update prices of the items. Most popular approaches to dynamic pricing use a
passive learning approach, where the algorithm uses historical data to learn
various parameters of the pricing problem, and uses the updated parameters to
generate a new set of prices. We show that one can use active learning
algorithms such as Thompson sampling to more efficiently learn the underlying
parameters in a pricing problem. We apply our algorithms to a real e-commerce
system and show that the algorithms indeed improve revenue compared to pricing
algorithms that use passive learning.
