Recommendation system with minimized transaction data

Hwangbo, Y., Lee, K. J., Jeong, B., & Park, K. Y. (2021). Recommendation system with minimized transaction data. Data Science and Management4, 40-45.(pdf)


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.

Deploying an Artificial Intelligence-based defect finder for manufacturing quality management

Lee, K. J., Kwon, J. W., Min, S., & Yoon, J. (2021). Deploying an Artificial Intelligence-based defect finder for manufacturing quality management. AI Magazine42(2), 5-18.(pdf)


This paper describes how the Big Data Research Center of Kyung Hee University
and Benple Inc. developed and deployed an artificial intelligence system
to automate the quality management process for Frontec, an SME company
that manufactures automobile parts. Various constraints, such as response time
requirements and the limited computing resources available, needed to be considered
in this project. Defect finders using large-scale images are expected to
classify weld nuts within 0.2 s with an accuracy rate of over 95%. Our system
uses Circular Hough Transform for preprocessing as well as an adjusted VGG
(Visual Geometry Group) model. Our convolutional neural network (CNN) system
shows an accuracy of over 99% and a response time of about 0.14 s. To embed
the CNN model into the factory, we reimplemented the preprocessing modules
using LabVIEW and had the classification model server communicate with an
existing vision inspector. We share our lessons from this experience by explaining
the procedure and real-world issues developing and embedding a deep learning
framework in an existing manufacturing environment without implementing
any hardware changes.