# Modeling Personalized Item Frequency Information for Next-basket Recommendation

arXiv: https://arxiv.org/abs/2006.00556 · HTML: https://mlsanity.com/paper/2006.00556/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 24 of 24)

- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest](https://mlsanity.com/paper/2306.00248/) (2023)
- [A Next Basket Recommendation Reality Check](https://mlsanity.com/paper/2109.14233/) (2021)
- [Attention Mixtures for Time-Aware Sequential Recommendation](https://mlsanity.com/paper/2304.08158/) (2023)
- [Repeat-Aware Neighbor Sampling for Dynamic Graph Learning](https://mlsanity.com/paper/2405.17473/) (2024)
- [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)
- [Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items](https://mlsanity.com/paper/2201.13313/) (2022)
- [A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems](https://mlsanity.com/paper/2309.07682/) (2023)
- [Learning Complex Users' Preferences for Recommender Systems](https://mlsanity.com/paper/2107.01529/) (2021)
- [Global-Local Item Embedding for Temporal Set Prediction](https://mlsanity.com/paper/2109.02074/) (2021)
- [Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders](https://mlsanity.com/paper/2211.09072/) (2022)
- [A Case Study of Next Portfolio Prediction for Mutual Funds](https://mlsanity.com/paper/2410.18098/) (2024)
- [Timing-Aware Repurchase Prediction for Web-Scale E-Commerce: Survival Models for Multi-Surface Grocery Recommendation](https://mlsanity.com/paper/2608.28393/) (2026)
- [Next Period Recommendation Reality Check](https://mlsanity.com/paper/2110.05589/) (2021)
- [Cross-domain User Preference Learning for Cold-start Recommendation](https://mlsanity.com/paper/2112.03667/) (2021)
- [CVTT: Cross-Validation Through Time](https://mlsanity.com/paper/2205.05393/) (2022)
- [Personalized Category Frequency prediction for Buy It Again recommendations](https://mlsanity.com/paper/2308.01195/) (2023)
- [GRAINRec: Graph and Attention Integrated Approach for Real-Time Session-Based Item Recommendations](https://mlsanity.com/paper/2411.09152/) (2024)
- [Producer-Fairness in Sequential Bundle Recommendation](https://mlsanity.com/paper/2506.20329/) (2025)
- [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)

Next-basket recommendation (NBR) is prevalent in e-commerce and retail industry. In this scenario, a user purchases a set of items (a basket) at a time. NBR performs sequential modeling and recommendation based on a sequence of baskets. NBR is in general more complex than the widely studied sequential (session-based) recommendation which recommends the next item based on a sequence of items. Recurrent neural network (RNN) has proved to be very effective for sequential modeling and thus been adapted for NBR. However, we argue that existing RNNs cannot directly capture item frequency information in the recommendation scenario. Through careful analysis of real-world datasets, we find that em personalized item frequency (PIF) information (which records the number of times that each item is purchased by a user) provides two critical signals for NBR. But, this has been largely ignored by existing methods. Even though existing methods such as RNN based methods have strong representation ability, our empirical results show that they fail to learn and capture PIF. As a result, existing methods cannot fully exploit the critical signals contained in PIF. Given this inherent limitation of RNNs, we propose a simple item frequency based k-nearest neighbors (kNN) method to directly utilize these critical signals. We evaluate our method on four public real-world datasets. Despite its relative simplicity, our method frequently outperforms the state-of-the-art NBR methods -- including deep learning based methods using RNNs -- when patterns associated with PIF play an important role in the data.