Repurchase behavior is a primary signal in large-scale retail recommendation, particularly in categories with frequent replenishment: many items in a user's next basket were previously purchased and their timing follows stable, item-specific cadences. Yet most next basket repurchase recommendation models represent history as a sequence of discrete basket events indexed by visit order, which cannot explicitly model elapsed calendar time or update item rankings as days pass between purchases. We present CASE (Cadence-Aware Set Encoding for next basket repurchase recommendation), which decouples item-level cadence learning from cross-item interaction, enabling explicit calendar-time modeling while remaining production-scalable. CASE represents each item's purchase history as a calendar-time signal over a fixed horizon, applies shared multi-scale temporal convolutions to capture recurring rhythms, and uses induced set attention to model cross-item dependencies with sub-quadratic complexity, allowing efficient batch inference at scale. Across three public benchmarks and a proprietary dataset, CASE consistently improves Precision, Recall, and NDCG at multiple cutoffs compared to strong next basket prediction baselines. In a production-scale evaluation with tens of millions of users and a large item catalog, CASE achieves up to 8.6% relative Precision and 9.9% Recall lift at top-5, demonstrating that scalable cadence-aware modeling yields measurable gains in both benchmark and industrial settings.

翻译：回购行为是大型零售推荐中的关键信号，尤其在频繁补货品类中——用户下次购物篮中的许多物品为之前购买过，且其购买时间遵循稳定的、物品特定的节奏。然而，现有的大多数下次购物篮回购推荐模型将历史记录表示为按访问顺序索引的离散购物篮事件序列，这无法显式建模经过的日历时间，也无法在购买间隔期间更新物品排序。我们提出CASE（节奏感知集合编码用于下次购物篮回购推荐），该方法将物品级节奏学习与跨物品交互解耦，在保持生产可扩展性的同时实现显式日历时间建模。CASE将每个物品的购买历史表示为固定时间范围内的日历时间信号，应用共享多尺度时间卷积捕捉重复节奏，并通过归纳集合注意力以次二次复杂度建模跨物品依赖关系，支持大规模高效批量推理。在三个公开基准数据集和一个专有数据集上，与强基线模型相比，CASE在多个截断值下持续提升精确率、召回率和NDCG。在涵盖数千万用户和大型物品目录的生产规模评估中，CASE在前5名推荐上实现了高达8.6%的相对精确率提升和9.9%的相对召回率提升，表明可扩展的节奏感知建模在基准测试和工业环境中均能带来可衡量的性能提升。