Deep Learning and User Consumption Trends Classification and Analysis Based on Shopping Behavior

Abstract

Driven by the wave of digitalization, the booming development of the e-commerce industry urgently requires in-depth analysis of user shopping behavior to improve service experience. In view of the limitations of traditional models in dealing with complex shopping scenarios, this study innovatively proposes a deep learning model: the VATA model (a combination of variational autoencoder, transformer, and attention mechanism). Through this model, the authors strive to classify and analyze user shopping behavior more accurately and intelligently. Variational autoencoder (VAE) can learn the potential representation of user personalized historical data, capture the implicit characteristics of shopping behavior, and improve the ability to deal with actual shopping situations. Transformer models can more comprehensively capture the dependencies between shopping behaviors and understand shopping. The overall structure of behavior plays an important role.