Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs

By Mengting Wan, Di Wang, Matthew Goldman, Matt Taddy, Justin M. Rao, Jie Liu, Dimitrios Lymberopoulos, Julian McAuley. 2017

63 citations recorded by the source corpus.

In order to match shoppers with desired products and provide personalized promotions, whether in online or offline shopping worlds, it is critical to model both consumer preferences and price sensitivities simultaneously. Personalized preferences have been thoroughly studied in the field of recommender systems, though price (and price sensitivity) has received relatively little attention. At the same time, price sensitivity has been richly explored in the area of economics, though typically not in the context of developing scalable, working systems to generate recommendations. In this study, we seek to bridge the gap between large-scale recommender systems and established consumer theories from economics, and propose a nested feature-based matrix factorization framework to model both preferences and price sensitivities. Quantitative and qualitative results indicate the proposed personalized, interpretable and scalable framework is capable of providing satisfying recommendations (on two datasets of grocery transactions) and can be applied to obtain economic insights into consumer behavior.

Subfields: Recommender Systems, Economics & Operations Research

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Citations in the corpus

Machine Learning Methods That Economists Should Know About (2019, background): “1 Goals The traditional approach in econometrics, as exemplified in leading texts such as Wooldridge [2010], Angrist and Pischke [2008], Greene [2000] is to specify a target, an estimand, that is a functional of a joint distribution of the data.”
Counterfactual inference for consumer choice across many product categories (2019, methodology): “Wan et al. (2017) propose an alternative model for consumer grocery demand estimation which uses a latent factorization model with three stages.”
SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements (2017, methodology): “Matrix factorization methods have been used for web services recommendations (Stern, Herbrich and Thore, 2009) and also market basket data (Wan et al., 2017).”
Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data (2018, methodology): “Our estimation strategy is drawn from approaches developed in Athey et al. (2017) and Ruiz, Athey and Blei (2017), both of which considered the problem of choosing items from a supermarket, and it also relates to Wan et al. (2017), who take a matrix factorization approach to consumer choice.”
The Impact of Machine Learning on Economics (2018, background): “Another recent paper that makes use of factorization in the context of a structural model of consumer demand is Wan et al. (2017).”