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March 2020 SHOPPER: A probabilistic model of consumer choice with substitutes and complements
Francisco J. R. Ruiz, Susan Athey, David M. Blei
Ann. Appl. Stat. 14(1): 1-27 (March 2020). DOI: 10.1214/19-AOAS1265

Abstract

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.

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Francisco J. R. Ruiz. Susan Athey. David M. Blei. "SHOPPER: A probabilistic model of consumer choice with substitutes and complements." Ann. Appl. Stat. 14 (1) 1 - 27, March 2020. https://doi.org/10.1214/19-AOAS1265

Information

Received: 1 November 2017; Revised: 1 March 2019; Published: March 2020
First available in Project Euclid: 16 April 2020

zbMATH: 07200159
MathSciNet: MR4085081
Digital Object Identifier: 10.1214/19-AOAS1265

Keywords: causal interventions , discrete choice model , market basket data , Probabilistic modeling , variational inference

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.14 • No. 1 • March 2020
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