Newest papers

Borrowing Strength Across Stores: Transfer Learning for Choice-Based Revenue Management

Recep Yusuf Bekci, Stefanus Jasin, Joline Uichanco · 2026 · arXiv

DOI: 10.48550/arXiv.2610.28708 · open-access PDF

Japanese

大規模な小売チェーンは、地域の顧客の好みに応じてアソリューションおよび価格決定を調整しなければなりませんが、広範な販売記録を持つ小売業者でさえも、特に短い販売ウィンドウを持つ製品カテゴリーにおいて、店舗または地元の市場レベルで需要を推定するためのデータは限られています。

Machine translation (Meta M2M100); the English original follows.

Abstract

Large retail chains must tailor assortment and pricing decisions to local customer preferences, yet even retailers with extensive sales records often have limited data for estimating demand at the store or local market level, particularly in product categories with short selling windows. Local estimation (fitting a demand model for each store on its own) can therefore be unreliable, while pooling data across stores improves estimation precision at the cost of ignoring local demand heterogeneity. We study how retailers can balance these competing effects through transfer learning under a multin

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