arXiv AI
Aug 14
Demand Transfer Estimation at Scale via Restricted Logit Modeling
arXiv:2608. 12680v1 Announce Type: cross Abstract: Item demand forecasting is an integral component of store assortment optimization.
Borrowing Strength Across Stores: Transfer Learning for Choice-Based Revenue Management
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.
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arXiv AI
Oct 12
AI-Mediation in Retail Markets: Revenue Effects and Assortment Optimization
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arXiv Machine Learning
Jul 3
Learning Consumer Preferences from Bundle Sales Data
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arXiv Machine Learning
Aug 31
Robust Assortment Optimization from Observational Data
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.
arXiv Machine Learning
Sep 22
Combinatorial Inference on the Optimal Assortment in Multinomial Logit Models
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...
arXiv Statistics ML
Sep 16
Learning Choice Model Trees for Feature-Based Multi-Product Pricing: Exact Optimization and Field Evidence
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...
arXiv AI
Oct 15
Better Capturing Interactions Between Products in~Retail: Revisited Negative Sampling for~Basket Choice Modeling
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.
arXiv Machine Learning
Aug 26
Across-Design Uncertainty in Short Pricing Panels: Inference and Identification
arXiv:2608.21334v2 Announce Type: replace Abstract: Short observational pricing panels often contain many observations but few distinct price movements. We evaluate the inferential consequences of th...
arXiv AI
Jul 16
Tabular Foundation Models for Discrete Choice Estimation
arXiv:2607. 13314v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) generate predictions on structured data via in-context learning, without task-specific estimation.
arXiv Machine Learning
Jun 10
Data-Driven Dynamic Assortment in Online Platforms: Learning about Two Sides
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.
arXiv Machine Learning
6d ago
Source-Aware Dynamic Pricing with Strategic Buyers
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arXiv AI
Aug 5
Minimax-Optimal Semiparametric Contextual Dynamic Pricing with Multimodal Revenue
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