Recommendation system with minimized transaction data

Abstract

This paper deals with the recommendation system in the so-called user-centric payment environment where users, i.e., the payers, can make payments without providing self-information to merchants. This service maintains only the minimum purchase information such as the purchased product names, the time of purchase, the place of purchase for possible refunds or cancellations of purchases. This study aims to develop AI-based recommendation system by utilizing the minimum transaction data generated by the user-centric payment service. First, we developed a matrix-based extrapolative collaborative filtering algorithm based on open transaction data. The recommendation methodology was verified with the real transaction data. Based on the experimental results, we confirmed that the recommendation performance is satisfactory only with the minimum purchase information.

Keywords

• User-centric payment
• Recommendation service
• Artificial intelligence
• Extrapolative collaborative filtering