Research on Customer Purchase Intention Prediction Methods for E-commerce Platforms Based on User Behavior Data

Abstract

The exponential growth of e-commerce platforms has generated vast amounts of user behavior data, presenting unprecedented opportunities for predicting customer purchase intentions. This research investigates advanced machine learning methodologies for analyzing user behavioral patterns and developing accurate prediction models. We propose a comprehensive framework that integrates feature engineering techniques, ensemble learning algorithms, and real-time prediction systems to enhance purchase intention forecasting accuracy. Our experimental evaluation demonstrates that the proposed methodology achieves superior performance compared to existing approaches, with Random Forest and Gradient Boosting models showing particularly promising results. The framework successfully processes multi-dimensional user interaction data including clickstream patterns, session characteristics, and temporal behavior sequences. Through extensive validation on real-world e-commerce datasets, our approach demonstrates significant improvements in prediction accuracy while maintaining computational efficiency suitable for large-scale deployment.