Assortment Optimization under Unknown MultiNomial Logit Choice Models

Motivated by e-commerce, we study the online assortment optimization problem. The seller offers an assortment, i.e. a subset of products, to each arriving customer, who then purchases one or no product from her offered assortment. A customer’s purchase decision is governed by the underlyingMultiNomial Logit (MNL) choice model. The seller aims to maximize the total revenue in a finite sales horizon, subject to resource constraints and uncertainty in the MNL choice model. We first propose an efficient online policy which incurs a regret Õ(T ), where T is the number of customers in the sales horizon. Then, we propose a UCB policy that achieves a regret Õ(T ). Both regret bounds are sublinear in the number of assortments.