# A Next Basket Recommendation Reality Check

arXiv: https://arxiv.org/abs/2109.14233 · HTML: https://mlsanity.com/paper/2109.14233/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 29 Sep 2021.

## Builds on (most cited first, 11 of 11)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research](https://mlsanity.com/paper/1911.07698/) (2019)
- [Critically Examining the "Neural Hype": Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models](https://mlsanity.com/paper/1904.09171/) (2019)
- [Modeling Personalized Item Frequency Information for Next-basket Recommendation](https://mlsanity.com/paper/2006.00556/) (2020)
- [User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets](https://mlsanity.com/paper/2010.01470/) (2020)
- [Predicting Temporal Sets with Deep Neural Networks](https://mlsanity.com/paper/2006.11483/) (2020)
- [How to Measure the Reproducibility of System-oriented IR Experiments](https://mlsanity.com/paper/2010.13447/) (2020)
- [Modeling Dynamic Attributes for Next Basket Recommendation](https://mlsanity.com/paper/2109.11654/) (2021)
- [Optimizing Ranking Models in an Online Setting](https://mlsanity.com/paper/1901.10262/) (2019)

## Built on by (most cited first, 15 of 15)

- [Large Language Models as Zero-Shot Conversational Recommenders](https://mlsanity.com/paper/2308.10053/) (2023)
- [RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation](https://mlsanity.com/paper/2306.08947/) (2023)
- [Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping](https://mlsanity.com/paper/2308.01308/) (2023)
- [Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation](https://mlsanity.com/paper/2408.16578/) (2024)
- [CASE: Cadence-Aware Set Encoding for Large-Scale Next Basket Repurchase Recommendation](https://mlsanity.com/paper/2604.06718/) (2026)
- [Label Attention Network for Temporal Sets Prediction: You Were Looking at a Wrong Self-Attention](https://mlsanity.com/paper/2303.00280/) (2023)
- [To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders](https://mlsanity.com/paper/2310.14079/) (2023)
- [A Case Study of Next Portfolio Prediction for Mutual Funds](https://mlsanity.com/paper/2410.18098/) (2024)
- [A Reproducibility Study of Product-side Fairness in Bundle Recommendation](https://mlsanity.com/paper/2507.14352/) (2025)
- [T-REX: Transformer-Based Category Sequence Generation for Grocery Basket Recommendation](https://mlsanity.com/paper/2603.06631/) (2026)
- [Timing-Aware Repurchase Prediction for Web-Scale E-Commerce: Survival Models for Multi-Surface Grocery Recommendation](https://mlsanity.com/paper/2608.28393/) (2026)
- [LightGCN: Evaluated and Enhanced](https://mlsanity.com/paper/2312.16183/) (2023)
- [Set2Seq Transformer: Temporal and Position-Aware Set Representations for Sequential Multiple-Instance Learning](https://mlsanity.com/paper/2408.03404/) (2024)
- [NEST: Nested Event Stream Transformer for Sequences of Multisets](https://mlsanity.com/paper/2602.00520/) (2026)
- [Odds-Shift Slippage in One-vs-Rest Rankers: Diagnosing and Repairing Reweighting-Induced Top-K Errors](https://mlsanity.com/paper/2609.13810/) (2026)

## Abstract (arXiv)

The goal of a next basket recommendation (NBR) system is to recommend items for the next basket for a user, based on the sequence of their prior baskets. Recently, a number of methods with complex modules have been proposed that claim state-of-the-art performance. They rarely look into the predicted basket and just provide intuitive reasons for the observed improvements, e.g., better representation, capturing intentions or relations, etc. We provide a novel angle on the evaluation of next basket recommendation methods, centered on the distinction between repetition and exploration: the next basket is typically composed of previously consumed items (i.e., repeat items) and new items (i.e, explore items). We propose a set of metrics that measure the repeat/explore ratio and performance of NBR models. Using these new metrics, we analyze state-of-the-art NBR models. The results of our analysis help to clarify the extent of the actual progress achieved by existing NBR methods as well as the underlying reasons for the improvements. Overall, our work sheds light on the evaluation problem of NBR and provides useful insights into the model design for this task.