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:2609.16952v1 Announce Type: cross
Abstract: Feature-based multi-product pricing uses customer characteristics to identify demand heterogeneity and tailor prices across products. Choice model tr...
By Jiajie Zhang, Yanqiu Ruan, Xiao Jin, Chung Piaw Teo
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
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: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: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