SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements
This work addresses the problem of understanding consumer behavior for retailers, but it appears incremental as it builds on existing probabilistic modeling approaches.
The authors tackled the problem of modeling consumer choice with item interactions by developing SHOPPER, a sequential probabilistic model, and found that it accurately predicts shopping behavior under price changes and identifies complementary and substitutable product pairs.
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.