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Retail basket modeling enhanced with new product interaction insights · arXiv research

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new model for understanding shopping basket choices in retail by learning embeddings that represent interactions between products, prices, and stores. This model is designed to uncover sales patterns from large transaction datasets, using a negative sampling optimization loss to overcome computational challenges with numerous items. Experiments on synthetic data and a dataset from Fortenova Group, a major retail company in Southeastern Europe, demonstrate the model's efficiency and potential applications for category managers. AI

IMPACT This model could improve retail assortment optimization and promotion planning by better understanding product interactions and customer preferences.

RANK_REASON This is a research paper published on arXiv detailing a new model for retail basket choice. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Retail basket modeling enhanced with new product interaction insights · arXiv research

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This is a research paper published on arXiv detailing a new model for retail basket choice. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jules D\'esir, Vincent Auriau, Martin Mo\v{z}ina, Emmanuel Malherbe ·

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

    arXiv:2610.17963v1 Announce Type: cross 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 prom…

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