arXiv Machine Learning
Aug 31

Robust Assortment Optimization from Observational Data

The paper introduces a robust framework for assortment optimization that addresses distributional shifts in customer choice behavior. It demonstrates computational tractability when the nominal choice model is known and develops statistically optimal algorithms for the data‑driven setting, providing matching upper and lower bounds on sample complexity. The authors identify "robust item‑wise coverage" as the minimal data requirement for efficient robust learning, bridging robustness and statistical efficiency in assortment planning.

By Miao Lu, Yuxuan Han, Han Zhong, Zhengyuan Zhou, Jose Blanchet
arXiv Machine Learning
Aug 13

Diffusion-Based Data-Driven Assortment Optimization

arXiv:2608. 11419v1 Announce Type: new Abstract: Assortment optimization is a fundamental problem in revenue management, typically addressed using parametric choice models such as the multinomial logit (MNL) and its variants.

By Junyi Liao, Xiaohui Jiang, Zhengwei Tong, Ethan X. Fang, Vahid Tarokh
arXiv AI
Jun 30

Assortment Planning with Sponsored Products

arXiv:2402. 06158v2 Announce Type: replace-cross Abstract: In the rapidly evolving landscape of retail, assortment planning plays a crucial role in determining the success of a business.

By Shaojie Tang, Shuzhang Cai, Jing Yuan, Kai Han