# CASE: Cadence-Aware Set Encoding for Large-Scale Next Basket Repurchase Recommendation

arXiv: https://arxiv.org/abs/2604.06718 · HTML: https://mlsanity.com/paper/2604.06718/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Builds on (most cited first, 6 of 6)

- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Modeling Personalized Item Frequency Information for Next-basket Recommendation](https://mlsanity.com/paper/2006.00556/) (2020)
- [Predicting Temporal Sets with Deep Neural Networks](https://mlsanity.com/paper/2006.11483/) (2020)
- [A Next Basket Recommendation Reality Check](https://mlsanity.com/paper/2109.14233/) (2021)
- [Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping](https://mlsanity.com/paper/2308.01308/) (2023)
- [Scalable Permutation-Aware Modeling for Temporal Set Prediction](https://mlsanity.com/paper/2504.17140/) (2025)

## Built on by (most cited first, 4 of 4)

- [AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems](https://mlsanity.com/paper/2606.26859/) (2026)
- [ComboShoppingBench: Evaluating LLM Agents for Budget-Constrained Basket Shopping with Coupons](https://mlsanity.com/paper/2608.09282/) (2026)
- [Timing-Aware Repurchase Prediction for Web-Scale E-Commerce: Survival Models for Multi-Surface Grocery Recommendation](https://mlsanity.com/paper/2608.28393/) (2026)
- [Beyond Ranking Accuracy: Evaluating LLM-Cited Feature Rationales for Next Basket Repurchase Recommendation](https://mlsanity.com/paper/2608.30333/) (2026)

## Abstract (arXiv)

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 recommendation 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 lift and 9.9% relative recall lift at top-5, showing that scalable cadence-aware modeling yields measurable gains in both benchmark and industrial settings.