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

Key points

• Brick-and-mortar retailers face many different challenges that involve understanding thoroughly its products catalog and customer preferences.1
• 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.1
• Our model is built to uncover sales patterns from very large transaction datasets.1
• 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.1

Sources (1)

• [1]Better Capturing Interactions Between Products in~Retail: Revisited Negative Sampling for~Basket Choice Modeling
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 08:41 AM

  In this paper, we propose a model of shopping basket that learns embeddings to represent interactions between products, prices and stores.

  Brick-and-mortar retailers face many different challenges that involve understanding thoroughly its products catalog and customer preferences.

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