发表机构
Walmart Global Tech（沃尔玛全球科技）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于受限Logit建模的方法，可在百万级商品规模下准确估计需求转移系数，结合独立需求预测与同类商品可得性调整，提升大规模品类组合优化的需求预测效果。
AI 中文摘要
商品需求预测是门店品类组合优化的核心环节。现有研究聚焦于学习合适的顾客选择模型，并利用该模型针对某一品类组合提案计算目标函数（即预期需求）的取值。但对于拥有多类商品的大规模商品集合而言，该方法效率低下，需为每一种可能的商品品类组合单独进行需求预测。另一种可行方法结合了独立预测商品需求的高效性，同时对独立预测结果进行调整，以考虑商品需求与货架上其他同类商品可得性之间的关系。该方法的核心是需求转移（DT）系数的估计，这些DT系数表示：若某一目标商品（顾客进店打算购买的商品）被下架，其需求中会有百分之多少转移到集合中的其他每一种商品上。本文提出一种可在含100万以上商品的大规模商品集合上计算这些DT系数的方法。针对多地点、多类别的数据及历史交易数据开展的实验表明，当满足关于替代行为的某些合理假设时，本文提出的方法能够准确估计潜在DT系数，并提升需求预测的效果。
英文摘要
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.
Comments8 pages. Accepted in the Main Conference of IEEE ICMLA 2026
