Recep Yusuf Bekci, Stefanus Jasin, Joline Uichanco · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28708 · open-access PDF
대형 소매 체인은 지역 고객의 선호에 맞게 범위 및 가격 결정을 조정해야하지만, 광범위한 판매 기록을 가진 소매 업체조차도 가게 또는 지역 시장 수준에서, 특히 짧은 판매 창을 가진 제품 범주에서 수요를 추정할 수있는 데이터가 제한되어 있습니다.
Machine translation (Meta M2M100); the English original follows.
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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