Pcard: Personalized Restaurants Recommendation from Card Payment Transaction Records

Abstract

Personalized Point of Interest (POI) recommendation that incorporates users' personal preferences is an important subject of research. However, challenges exist such as dealing with sparse rating data and spatial location factors. As one of the biggest card payment organizations in the United States, our company holds abundant card payment transaction records with numerous features. In this paper, using restaurant recommendation as a demonstrating example, we present a personalized POI recommendation system (Pcard) that learns user preferences based on user transaction history and restaurants' locations. With a novel embedding approach that captures user embeddings and restaurant embeddings, we model pairwise restaurant preferences with respect to each user based on their locations and dining histories. Finally, a ranking list of restaurants within a spatial region is presented to the user. The evaluation results show that the proposed approach is able to achieve high accuracy and present effective recommendations.