We propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. The model learns log-demand as a smooth, context-conditioned function of log-prices, allowing elasticities to be derived exactly from the learned demand surface. On the Dominick's beer dataset, ICDN improves out-of-sample generalization over a directed log-log benchmark and yields more stable, economically plausible elasticity estimates, especially for weakly identified cross-price effects.

翻译：我们提出可积上下文相关需求网络（ICDN），这是一种面向多产品零售需求的需求优先神经模型。该模型将对数需求学习为对数价格的平滑、上下文条件函数，从而能够从学习到的需求曲面精确推导出弹性系数。在Dominick's啤酒数据集上，ICDN相比有向对数-对数基准方法提升了样本外泛化性能，并得到更稳定、经济上更合理的弹性估计，尤其是对于弱识别的交叉价格效应。