arXiv cs.AI· Jules D\'esir, Vincent Auriau, Martin Mo\v{z}ina, Emmanuel Malherbe·· 3 天前AI 评分18
面向零售购物篮选择建模的负采样改进：更好捕捉商品间交互
Better Capturing Interactions Between Products in~Retail: Revisited Negative Sampling for~Basket Choice Modeling
AI 导读
研究提出一种购物篮模型，通过学习嵌入向量来表征商品、价格与门店之间的交互关系，并采用基于实际在售商品组合的随机负采样来计算优化损失，以突破商品数量庞大带来的计算瓶颈。在合成数据上，该负采样策略效果优于已有文献方法；模型还在 Fortenova Group 数百万条真实交易数据上完成训练与评估，可用于支持品类管理人员的零售决策。
正文 · 原文
Abstract: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.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.17963 [cs.LG] |
| (or arXiv:2610.17963v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17963 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Machine Learning and Knowledge Discovery in Databases., 16021, pp.125-142, 2025, Lecture Notes in Computer Science, 978-3-032-06118-8 |
Submission history
From: Vincent Auriau [view email] [via CCSD proxy]
[v1]
Tue, 13 Oct 2026 08:41:22 UTC (493 KB)
来源：arXiv cs.AI · arxiv.org