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The paper introduces CASE, a novel next-basket recommendation model that explicitly models calendar time and item-specific purchase cadences to improve repurchase prediction. CASE uses multi-scale temporal convolutions to learn item-level cadences and induced set attention to efficiently model cross-item dependencies. Experiments on public benchmarks and a large-scale production dataset demonstrate that CASE significantly outperforms existing methods in Precision, Recall, and NDCG, achieving up to 9.9% Recall lift in a real-world setting.
Forget visit order: modeling item-specific purchase cadences with calendar time unlocks significant gains in next-basket repurchase prediction, even at production scale.
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.
