Assortment Optimization Under the Mallows model
Antoine Desir, Vineet Goyal, Srikanth Jagabathula, Danny Segev
Abstract
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.
BibTeX
@inproceedings{NIPS2016_466accba,
author = {Desir, Antoine and Goyal, Vineet and Jagabathula, Srikanth and Segev, Danny},
booktitle = {Advances in Neural Information Processing Systems},
editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Assortment Optimization Under the Mallows model},
url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/466accbac9a66b805ba50e42ad715740-Paper.pdf},
volume = {29},
year = {2016}
}