arXiv:2209. 04942v2 Announce Type: replace-cross Abstract: Problem definition: This paper studies the problem of estimating consumer preferences from bundle sales data.
By Ningyuan Chen, Setareh Farajollahzadeh, Qingwei Jin, Fanni Shen, Guan Wang
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. 11118v1 Announce Type: new Abstract: We study a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers in a discrete-time setting.
By Rahul Roy, Nur Sunar, Jayashankar M. Swaminathan
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. 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:2608. 12680v1 Announce Type: cross Abstract: Item demand forecasting is an integral component of store assortment optimization.
By Lakshya Garg, Deep Narayan Mishra, Swapnil Yadav, Haoan Wang, Sujal Alugubelli, Karthik Kumaran, Anupriya Sharma