# SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements

arXiv: https://arxiv.org/abs/1711.03560 · HTML: https://mlsanity.com/paper/1711.03560/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2017.

## Built on by (most cited first, 14 of 14)

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- [A Survey of Reasoning for Substitution Relationships: Definitions, Methods, and Directions](https://mlsanity.com/paper/2404.08687/) (2024)
- [Structured Nonparametric Variational Inference for Dependent Latent Modeling](https://mlsanity.com/paper/2606.15458/) (2026)
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## Abstract (arXiv)

We develop SHOPPER, a sequential probabilistic model of shopping data. SHOPPER uses interpretable components to model the forces that drive how a customer chooses products; in particular, we designed SHOPPER to capture how items interact with other items. We develop an efficient posterior inference algorithm to estimate these forces from large-scale data, and we analyze a large dataset from a major chain grocery store. We are interested in answering counterfactual queries about changes in prices. We found that SHOPPER provides accurate predictions even under price interventions, and that it helps identify complementary and substitutable pairs of products.