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：再购买行为是大规模零售推荐中的关键信号，尤其在高频复购品类中，用户下次购物篮中的许多商品均为历史购买项，且其购买时机遵循稳定的商品特定节律。然而，现有的大多数下次购物篮再购买推荐模型将历史记录建模为由访问顺序索引的离散购物篮事件序列，这类方法既无法显式建模日历时间间隔，也无法在跨天购买的间隙动态更新商品排序。我们提出CASE（节律感知集合编码）方法，通过解耦商品级节律学习与跨商品交互，在保持生产级可扩展性的同时实现显式日历时间建模。CASE将每个商品的购买历史表示为一个固定时间跨度的日历时间信号，应用共享多尺度时间卷积捕捉周期性节律，并通过归纳集合注意力以亚二次复杂度建模跨商品依赖关系，从而实现高效的大规模批处理推理。在三个公开基准数据集与一个专有数据集上，与强基线方法相比，CASE在多个截断值下的精确率、召回率与NDCG指标上均取得一致性提升。面向数千万用户与大型商品目录的生产级评估表明，CASE在top-5推荐上实现了高达8.6%的精确率提升与9.9%的召回率提升，验证了可扩展的节律感知建模在基准测试与工业场景中均能带来可量化的性能增益。