We consider the assortment optimization problem when customer preferences follow a mixture of Mallows distributions. The assortment optimization problem focuses on determining the revenue/profit maximizing subset of products from a large universe of products; it is an important decision that is commonly faced by retailers in determining what to offer their customers. There are two key challenges: (a) the Mallows distribution lacks a closed-form expression (and requires summing an exponential number of terms) to compute the choice probability and, hence, the expected revenue/profit per customer; and (b) finding the best subset may require an exhaustive search. Our key contributions are an efficiently computable closed-form expression for the choice probability under the Mallows model and a compact mixed integer linear program (MIP) formulation for the assortment problem.
|Number of pages
|Advances in Neural Information Processing Systems
|Published - 2016
|30th Annual Conference on Neural Information Processing Systems, NIPS 2016 - Barcelona, Spain
Duration: 5 Dec 2016 → 10 Dec 2016
Bibliographical notePublisher Copyright:
© 2016 NIPS Foundation - All Rights Reserved.
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing