We propose a data-driven approach to model assortment optimization problems based on three real data sets. Our work is motivated by two empirical observations from customers browsing history on Taobao: one is that most customers browse very few items (=5) before they make a purchase; the second...

Assortment optimization arises widely in many practical applications such as retailing and online advertising. In this problem, the goal is to select a subset from a universe of substitutable products to offer customers in order to maximize the expected revenue. We study a robust assortment...

This paper considers the scenario where the assortment provided by the seller can influence customers' evaluation of item utility. A possible consequence is that certain items in an assortment become "star items" to customers, and customers over-evaluate their utilities. We call such a...