# M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation

arXiv: https://arxiv.org/abs/2004.01646 · HTML: https://mlsanity.com/paper/2004.01646/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer](https://mlsanity.com/paper/2108.06625/) (2021)
- [Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation](https://mlsanity.com/paper/2106.06165/) (2021)
- [Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformer](https://mlsanity.com/paper/2210.13572/) (2022)
- [Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation](https://mlsanity.com/paper/2301.12197/) (2023)
- [Hypergraph Enhanced Knowledge Tree Prompt Learning for Next-Basket Recommendation](https://mlsanity.com/paper/2312.15851/) (2023)

## Abstract (arXiv)

Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M2) for the next-basket recommendation. This method models three important factors in next-basket generation process: 1) users' general preferences, 2) items' global popularities and 3) transition patterns among items. Unlike existing recurrent neural network-based approaches, M2 does not use the complicated networks to model the transitions among items, or generate embeddings for users. Instead, it has a simple encoder-decoder based approach (ed-Trans) to better model the transition patterns among items. We compared M2 with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket. Our experimental results demonstrate that M2 significantly outperforms the state-of-the-art methods on all the datasets in all the tasks, with an improvement of up to 22.1%. In addition, our ablation study demonstrates that the ed-Trans is more effective than recurrent neural networks in terms of the recommendation performance. We also have a thorough discussion on various experimental protocols and evaluation metrics for next-basket recommendation evaluation.