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Activity Number: 390 - The Best of the Annals of Applied Statistics (AOAS)
Type: Invited
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: IMS
Abstract #308132
Title: SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements
Author(s): Francisco Ruiz* and Susan Athey and David Blei
Companies: Deepmind and Stanford University and Columbia University
Keywords: probabilistic modeling; discrete choice model; counterfactual queries; variational inference; interpretable models
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.


Authors who are presenting talks have a * after their name.

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