Assortment

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Demand Transfer Estimation at Scale via Restricted Logit Modeling
Aug 13, 2026Lakshya Garg, Deep Narayan Mishra, Swapnil Yadav +4Assortment
Walmart Global Tech, Sunnyvale, CA, USA
Diffusion-Based Data-Driven Assortment Optimization
Aug 11, 2026Junyi Liao, Xiaohui Jiang, Zhengwei Tong +2Assortment Data-Driven
Department of Electrical and Computer Engineering, Duke University · Department of Biostatistics and Bioinformatics, Duke University · Department of Computer Science, Duke University
Diversified Multinomial Logit Contextual Bandits
Jul 13, 2026Heesang Ann, Taehyun Hwang, Min-hwan OhAssortment Stochastic Multi-Armed Bandits
Seoul National University
Estimation, Prediction, and Assortment Optimization for Markov Chain Choice Models with Panel Data
Jul 10, 2026Yalcin Akcay, Gerardo Berbeglia, Young-San LinAssortment Discrete Choice Models
Melbourne Business School
Data-Driven Dynamic Assortment in Online Platforms: Learning about Two Sides
Jun 9, 2026Rahul Roy, Nur Sunar, Jayashankar M. SwaminathanAssortment Optimality
IE Business School, IE University, Madrid 28006 · Kenan-Flagler Business School, The University of North Carolina at Chapel Hill, NC 27599
Constrained user-item allocation for e-commerce marketing campaigns
Jun 8, 2026Maja Lindström, Natalija Glisovic, Jan von Pichowski +2Sales Lead User Modeling
Department of Computing Science Integrated Science Lab Umeå University SE-901 87 Umeå, Sweden · KTH Royal Institute of Technology Stockholm, SE-114 28, Sweden · Chair of Machine Learning for Complex Networks Center for Artificial Intelligence and Data Science (CAIDAS) University of Würzburg DE-97070 Würzburg, Germany +2
Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification
May 25, 2026Joongkyu Lee, Min-hwan OhAssortment Stochastic Multi-Armed Bandits
Seoul National University, Seoul, Korea.
Reinforcement Learning for Graph Generation under a Hard Assortativity Constraint
May 22, 2026Hoyun Choi, Junghyo Jo, Deok-Sun LeeInhomogeneous Random Graphs Network Science
School of Computational Sciences, Korea Institute for Advanced Study, 85, Hoegi-ro, 02455, Seoul, Korea. · Department of Physics Education, Seoul National University, 1, Gwanak-ro, 08826, Seoul, Korea. · Center for Theoretical Physics and Artificial Intelligence Institute, Seoul National University, 1, Gwanak-ro, 08826, Seoul, Korea. +1
Learning in Position-Aware Multinomial Logit Bandits: From Multiplicative to General Position Effects
May 17, 2026Xi Chen, Shibo Dai, Jiameng Lyu +1Assortment Stochastic Multi-Armed Bandits
Leonard N. Stern School of Business, New York University, New York, NY 10012, USA · Qiuzhen College, Tsinghua University, Beijing 100084, China · Department of Management Science, School of Management, Fudan University, Shanghai, 200433, China +1
Optimal Exploration of New Products under Assortment Decisions
Apr 20, 2026Jackie Baek, Atanas Dinev, Thodoris LykourisAssortment Exploration
Stern School of Business, New York University · Massachusetts Institute of Technology
Assortment Planning with Sponsored Products
Feb 9, 2024Shaojie Tang, Shuzhang Cai, Jing Yuan +1Assortment Planning
Naveen Jindal School of Management, The University of Texas at Dallas · Department of Computer Science and Engineering, The University of North Texas · School of Computer Science and Technology, Soochow University
Combinatorial Inference on the Optimal Assortment in Multinomial Logit Models
Jan 28, 2023Shuting Shen, Xi Chen, Ethan X. Fang +1Assortment Uncertainty Quantification
Department of Biostatistics, Harvard T.H. Chan School of Public Health · Leonard N. Stern School of Business, New York University · Department of Biostatistics & Bioinformatics, Duke University

Aug 13, 2026, cs.LGJ/K move · Enter open · S save

Demand Transfer Estimation at Scale via Restricted Logit Modeling

Lakshya Garg, Deep Narayan Mishra, Swapnil Yadav, Haoan Wang+3

Walmart Global Tech, Sunnyvale, CA, USA

Assortment

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Item demand forecasting is an integral component of store assortment optimization. Existing literature focuses on learning a suitable customer choice model and using this model to determine the value of an objective function (i.e. expected demand) with respect to an assortment proposal. However, for large item universe with many categories, this approach can prove inefficient, needing a separate demand forecast for every possible item assortment. An alternate approach exists whereby we combine the efficiency of forecasting item demand independently, while at the same time applying adjustments to the independent forecasts that account for the relations between item demand and the availability of other similar items on the shelf. Central to this approach is the estimation of Demand Transfer (DT) coefficients. These DT coefficients represent the percent of a particular target item's (item that the customer walked in the store to buy) demand that is redirected to each other item in the universe should the target item be removed from the shelf. We introduce an approach that allows us to compute these DT coefficients on large item universes (assortments having 1 million+ items). Experiments on data as well as historical transaction data for multiple locations within categories demonstrate that when certain reasonable assumptions about substitution behavior are satisfied, our procedure is able to accurately estimate underlying DT coefficients and lead to improvements in demand forecasting.

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