# Assortment Optimization under Unknown MultiNomial Logit Choice Models

arXiv: https://arxiv.org/abs/1704.00108 · HTML: https://mlsanity.com/paper/1704.00108/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2017.

## Built on by (most cited first, 5 of 5)

- [Multinomial Logit Contextual Bandits: Provable Optimality and Practicality](https://mlsanity.com/paper/2103.13929/) (2021)
- [Learning an Optimal Assortment Policy under Observational Data](https://mlsanity.com/paper/2502.06777/) (2025)
- [MNL-Bandit with Knapsacks: a near-optimal algorithm](https://mlsanity.com/paper/2106.01135/) (2021)
- [Multi-User Contextual Cascading Bandits for Personalized Recommendation](https://mlsanity.com/paper/2508.13981/) (2025)
- [MNL-Bandits under Inventory and Limited Switches Constraints](https://mlsanity.com/paper/2204.10787/) (2022)

## Abstract (arXiv)

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 underlying MultiNomial 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 tildeO(T2/3), where T is the number of customers in the sales horizon. Then, we propose a UCB policy that achieves a regret tildeO(T1/2). Both regret bounds are sublinear in the number of assortments.