Profiling US Restaurants from Billions of Payment Card Transactions

TL;DR

This paper introduces a novel framework that infers restaurant cuisine types in the US from transaction data alone, achieving over 76% accuracy, which can enhance merchant profiling without external data collection.

Contribution

The study presents the first method to accurately infer restaurant cuisine types solely from transaction data using deep learning techniques.

Findings

Achieved 76.2% accuracy in classifying restaurant cuisines.

Developed a three-step framework combining weak supervision, feature extraction, and neural networks.

Demonstrated the feasibility of inferring merchant attributes from transaction data alone.

Abstract

A payment card (such as debit or credit) is one of the most convenient payment methods for purchasing goods and services. Hundreds of millions of card transactions take place across the globe every day, generating a massive volume of transaction data. The data render a holistic view of cardholder-merchant interactions, containing insights that can benefit various applications, such as payment fraud detection and merchant recommendation. However, utilizing these insights often requires additional information about merchants missing from the data owner's (i.e., payment company's) perspective. For example, payment companies do not know the exact type of product a merchant serves. Collecting merchant attributes from external sources for commercial purposes can be expensive. Motivated by this limitation, we aim to infer latent merchant attributes from transaction data. As proof of concept, we concentrate on restaurants and infer the cuisine types of restaurants from transactions. To this end, we present a framework for inferring the cuisine types of restaurants from transaction data. Our proposed framework consists of three steps. In the first step, we generate cuisine labels for a limited number of restaurants via weak supervision. In the second step, we extract a wide variety of statistical features and neural embeddings from the restaurant transactions. In the third step, we use deep neural networks (DNNs) to infer the remaining restaurants' cuisine types. The proposed framework achieved a 76.2% accuracy in classifying the US restaurants. To the best of our knowledge, this is the first framework to infer the cuisine types of restaurants by analyzing transaction data as the only source.