Thompson Sampling for Dynamic Pricing
This addresses revenue optimization for e-commerce platforms, but it is incremental as it applies known algorithms to a specific domain.
The paper tackled dynamic pricing in e-commerce by applying active learning algorithms like Thompson sampling to more efficiently learn underlying parameters, resulting in improved revenue compared to passive learning approaches.
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.