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:2606. 16183v1 Announce Type: cross Abstract: We develop an LLM-powered virtual population model that simulates demand for pricing decisions, in settings where products are described by rich unstructured information, such as text descriptions and images, and where decision makers need not only mean-demand predictions but also uncertainty estimates for counterfactual prices.
By Chengpiao Huang, Kaizheng Wang
arXiv:2607. 09817v1 Announce Type: new Abstract: We propose a framework for the Markov chain (MC) choice model with panel data, including parameter estimation, personalized choice prediction, and personalized assortment optimization.
By Yalcin Akcay, Gerardo Berbeglia, Young-San Lin
arXiv:2608. 16699v1 Announce Type: cross Abstract: Motivated by modern marketplaces, where the platform or the seller routinely gathers detailed user profiles, we study a novel learning theoretic model that simultaneously involves information and mechanism design.
By Maria-Florina Balcan, Tejas Pagare, Karan Singh
arXiv:2301.12254v5 Announce Type: replace-cross
Abstract: Assortment optimization has received active explorations in the past few decades due to its practical importance. Despite the extensive liter...
By Shuting Shen, Xi Chen, Ethan X. Fang, Junwei Lu
arXiv:2608.30944v1 Announce Type: new
Abstract: In online retailing, when a product sells out, a retailer often sees only the units sold, not how many customers would have bought it had inventory bee...
By Zean Han, Jing Liang, Ruihan Lin, Zezhen Ding, Jiheng Zhang