# Better Capturing Interactions Between Products in~Retail: Revisited Negative Sampling for~Basket Choice Modeling

arXiv: https://arxiv.org/abs/2610.17963 · HTML: https://mlsanity.com/paper/2610.17963/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 39 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Builds on (most cited first, 19 of 19)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
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- [Label-Embedding for Image Classification](https://mlsanity.com/paper/1503.08677/) (2015)
- [Entity Embeddings of Categorical Variables](https://mlsanity.com/paper/1604.06737/) (2016)
- [CARTE: Pretraining and Transfer for Tabular Learning](https://mlsanity.com/paper/2402.16785/) (2024)
- [Translation-based Recommendation](https://mlsanity.com/paper/1707.02410/) (2017)
- [Understanding graph embedding methods and their applications](https://mlsanity.com/paper/2012.08019/) (2020)
- [Beyond One-hot Encoding: lower dimensional target embedding](https://mlsanity.com/paper/1806.10805/) (2018)
- [A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability](https://mlsanity.com/paper/2002.00922/) (2020)
- [Does Negative Sampling Matter? A Review with Insights into its Theory and Applications](https://mlsanity.com/paper/2402.17238/) (2024)
- [SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements](https://mlsanity.com/paper/1711.03560/) (2017)
- [Embedding Compression in Recommender Systems: A Survey](https://mlsanity.com/paper/2408.02304/) (2024)
- [Representing Random Utility Choice Models with Neural Networks](https://mlsanity.com/paper/2207.12877/) (2022)
- [Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance](https://mlsanity.com/paper/2109.12042/) (2021)
- [Rethinking travel behavior modeling representations through embeddings](https://mlsanity.com/paper/1909.00154/) (2019)
- [RUMBoost: Gradient Boosted Random Utility Models](https://mlsanity.com/paper/2401.11954/) (2024)
- [Modeling Choice via Self-Attention](https://mlsanity.com/paper/2311.07607/) (2023)
- [Torch-Choice: A PyTorch Package for Large-Scale Choice Modeling with Python](https://mlsanity.com/paper/2304.01906/) (2023)

## Abstract (arXiv)

Brick-and-mortar retailers face many different challenges that involve understanding thoroughly its products catalog and customer preferences. In particular, assortment optimization - proposing the ideal mix of products - and promotion planning hold a pivotal role in their strategy. By leveraging sales data, retailers can make informed decisions on which products to sell and how to manage inventory, based on customer preferences as well as regional and seasonal trends. It is especially crucial to capture interactions between products, in order to minimize the number of items that cannibalize each other's sales and to ensure that complementary products, which are often purchased together, are conjointly available and sold across all stores. In this paper, we propose a model of shopping basket that learns embeddings to represent interactions between products, prices and stores. Our model is built to uncover sales patterns from very large transaction datasets. In particular, the optimization loss is computed with random negative samples in order to overcome the computational bottlenecks that arise with large number of items. Our experiments on synthetic data show the efficiency of drawing such negative samples based on the actual assortment of available products, with better results than approaches from the literature. We also validate our approach by training and evaluating our model on a dataset composed of millions of transactions from Fortenova Group, one of the leading retail company in Southeast Europe. Our model showcases promising applications in the sector of retail, with enriched interfaces to efficiently support category managers.