Application of multi-source heterogeneous analysis in user behavior prediction

Abstract

This study aims to accurately predict user lifecycle stages and behaviors using multi-source heterogeneous datasets. The core question is how to integrate various user attributes and interactions to effectively predict engagement and behavior patterns. This study develops a
comprehensive predictive model by combining multiple machine learning models to improve accuracy. The approach involves using user demographics, interaction patterns, and feedback characteristics to create separate predictive models, which are then integrated through ensemble learning methods such as stacking. Key performance metrics such as accuracy, precision, recall, and F1 score evaluate the effectiveness
of the model. The findings show that tailoring the user experience based on attributes such as age, gender, location, device type and browsing
patterns can significantly increase engagement and retention. Additionally, advertising strategies tailored to user preferences can increase conversion rates and satisfaction. The combined model outperforms the single model in terms of prediction accuracy and overall performance.