Recep Yusuf Bekci, Stefanus Jasin, Joline Uichanco · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28708 · open-access PDF
大型零售链必须根据当地客户的偏好定制分类和定价决策,但即使有丰富的销售记录的零售商也经常有有限的数据来估计商店或当地市场水平的需求,尤其是在销售窗口短的产品类别中。
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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