User behavior prediction model based on big data analysis

Abstract

A federated learning-based privacy-preserving prediction model is proposed for large-scale e-commerce user behavior analytics. The framework enables multiple e-commerce platforms to collaboratively train predictive models without transmitting raw user data, addressing the dual demand for high accuracy and robust data privacy. The main components of the system include secure aggregation protocols, which protect local model updates from exposure; differential privacy mechanisms, which inject controlled noise during distributed training to obscure individual user contributions. The system architecture supports asynchronous client participation, dynamic scaling, and heterogeneous feature engineering across organizational boundaries, which facilitates deployment in real-world e-commerce environments. Experimental validation conducted in multi-isolated environments shows that the federated method is as accurate as centralized models while significantly reducing privacy leakage. In addition, the model maintains a high recall rate in rare event scenarios and is able to balance privacy budget and prediction performance. Empirical risk assessment indicates that even with an increase in the number of participants or data diversity, privacy protection is significantly enhanced while maintaining analytical utility. The results demonstrate that federated learning is a scalable and effective method that can achieve privacy compliance in e-commerce analytics within data-restricted environments, and it lays a solid foundation for secure distributed business intelligence.