User Settlement/Churn Prediction on a CtoC service

Abstract

Recently, the form of transaction between consumers called CtoC (Consumer to Consumer) service has expandedthe market scale and has attracted great interest. In CtoC services, where the decrease in users directly causesthe decline of services, it is necessary to take measures to prevent existing users from leaving. In order to supportsuch measures, the problem of predicting user departure with high accuracy and interpretability has been activelystudied as "User Churn Prediction". The purpose of this research is to construct a high-performing and interpretableframework to predict user settlement and churn that incorporates not only users' own characteristics but also theeffects of contact between users. We applied Graph Neural Networks(GNNs), which have been attracting attentionin recent years, to the task of predicting user settlement and churn in the CtoC service.