Merchant Recommender System Using Credit Card Payment Data

Abstract

As the size of the domestic credit card market is steadily growing, the marketing method for credit card companies to secure customers is also changing. The process of understanding individual preferences and payment patterns has become an essential element, and it has developed a sophisticated personalized marketing method to properly understand customers’ interests and meet their needs. Based on this, a personalized system that recommends products or stores suitable for customers acts to attract customers more effectively. However, the existing research model implementing the General Framework using the neural network cannot reflect the major domain information of credit card payment data when applied directly to store recommendations. This study intends to propose a model specializing in the recommendation of member stores by reflecting the domain information of credit card payment data. The customers’ gender and age information were added to the learning data. The industry category and region information of the settlement member stores were reconstructed to be learned together with interaction data. A personalized recommendation system was realized by combining historical card payment data with customer and member store information to recommend member stores that are highly likely to be used by customers in the future. This study’s proposed model (NMF_CSI) showed a performance improvement of 3% based on HR@10 and 5% based on NDCG@10, compared to previous models. In addition, customer coverage was expanded so that the recommended model can be applied not only to customers actively using credit cards but also to customers with low usage data.