This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer's vector of individual features; the true set of individual demand-generating parameters is...

This paper studies the classic price-based network revenue management (NRM) problem with demand learning. The retailer dynamically decides prices of n products over a finite selling season (of length T) subject to m resource constraints, with the purpose of maximizing the cumulative revenue. In...

This paper proposes a general framework/meta-policy to solve Revenue Management (RM) problems with demand learning and potentially large action space, constrained by initial unreplenishable resources. This framework combines the technique of primal-dual method in optimization and...

We consider a multi-product dynamic pricing problem with limited inventories under the so-called Cascade Click model, which is one of the most popular click models used in practice for analyzing customers' click-and-search behavior in large-scale web analytic applications. We present three...