The paper proposes a shrinkage estimator that blends store‑specific demand estimates with pooled information from other stores under a multinomial logit model, addressing the trade‑off between local data scarcity and cross‑store heterogeneity. By leveraging self‑concordant‑like properties of the MNL likelihood, the authors derive finite‑sample performance guarantees and regret bounds for assortment and pricing decisions. Numerical experiments and an empirical study on European apparel retailer data show that this transfer‑learning approach improves out‑of‑sample product prioritization and revenue capture compared to both local estimation and full pooling.
By Recep Yusuf Bekci, Stefanus Jasin, Joline Uichanco
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: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: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
arXiv:2510. 12049v4 Announce Type: replace-cross Abstract: We quantify the short-term impact of Generative Artificial Intelligence (GenAI) on sales performance through a series of large-scale randomized field experiments involving millions of users and products at a leading cross-border online retail platform.
By Lu Fang, Zhe Yuan, Kaifu Zhang, Dante Donati, Miklos Sarvary
arXiv:2608. 08395v1 Announce Type: cross Abstract: Generative AI is shifting digital commerce from browsing toward agentic search, in which consumers delegate product discovery to AI agents.
By Lingxiu Dong, Kaiwen Luo, Fasheng Xu
The paper presents a basket‑choice model that learns embeddings to capture interactions among products, prices, and stores, using random negative sampling to handle large item sets. Experiments on synthetic data demonstrate that sampling based on the actual assortment improves performance over existing methods. The model is validated on millions of transactions from Fortenova Group, showing potential to aid category managers in retail decision‑making.
By Jules D\'esir, Vincent Auriau, Martin Mo\v{z}ina, Emmanuel Malherbe
arXiv:2607. 09956v1 Announce Type: new Abstract: Pricing food products to balance profitability with consumer welfare is a central challenge for retailers.
By Sujay Uday Rittikar
The paper introduces DCEO, a data‑driven framework that learns item‑level proxy scores directly aligned with long‑term user objectives in e‑commerce search. It aggregates these scores into a user‑level metric, measures alignment via relative causal effect, and uses an actor‑critic model to generate context‑dependent fusion weights for multiple objectives. Offline experiments and a 41‑day online A/B test show DCEO improves GMV by 0.36% over traditional proxies.
By Junzhao Zhang, Tao Zhang, Liren Yu, Feiyi Dong, Zhixuan Zhang, Dan Ou, Haihong Tang
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: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