Prediction Model of E-commerce Users' Purchase Behavior Based on Deep Learning

Abstract

This study mainly uses Deep Learning (DL) technology to build a prediction model of e-commerce users' purchasing behavior, and evaluates its application effect on actual e-commerce data. Methodologically, Recurrent Neural Network (RNN) is used to capture the temporal dependence of user behavior, and the performance of the model is improved through detailed data preprocessing and feature engineering. The research results show that the DL model based on RNN has achieved remarkable advantages in predicting the purchase behavior of e-commerce users. Compared with other methods, RNN model can capture the temporal dependence of user behavior more accurately, thus improving the prediction accuracy. This advantage is especially obvious when dealing with complex and dynamic user behavior data. It provides new ideas and methods for accurate marketing and personalized service of e-commerce industry.