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

By Francisco J. R. Ruiz, Susan Athey, David M. Blei. 2017

100 citations recorded by the source corpus.

We develop SHOPPER, a sequential probabilistic model of market baskets. 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.

Subfields: Management Science and Operations Research, Marketing, Probabilistic & Bayesian Methods, Economics & Operations Research, Signal Processing

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Citations in the corpus

Machine Learning Methods That Economists Should Know About (2019, methodology): “Varian [2014] is an early high level discussion of a selection of important ML methods.”
Counterfactual inference for consumer choice across many product categories (2019, background): “11See Ruiz et al. (2020) for a related paper that uses a heuristic approach to identify potential substitutes and complements automatically based on patterns of co-purchase.”
Understanding Large-Scale Dynamic Purchase Behavior (2020, unknown): “Our model also differs substantially from Ruiz et al. (2020) in terms of the interpretation of the latent spaces.”