User Behavior Feature Extraction and Optimization Methods for Mobile Advertisement Recommendation

Abstract

Mobile advertising has emerged as a dominant force in digital marketing, necessitating sophisticated approaches to understand and predict user behavior patterns. This research presents a comprehensive framework for extracting and optimizing user behavior features specifically designed for mobile advertisement recommendation systems. The proposed methodology integrates multi-dimensional data collection techniques with advanced feature engineering algorithms to enhance click-through rate prediction accuracy. Through extensive experimentation on real-world mobile advertising datasets, our approach demonstrates significant improvements in recommendation performance compared to traditional methods. The framework incorporates temporal behavior analysis, contextual feature extraction, and adaptive optimization algorithms that dynamically adjust to changing user preferences. Experimental results show that the proposed feature extraction methods achieve a 15.3% improvement in CTR prediction accuracy and a 12.7% increase in conversion rates. The optimization framework successfully reduces computational overhead while maintaining high prediction quality, making it suitable for real-time mobile advertising applications. These findings contribute to the advancement of personalized mobile advertising systems and provide practical insights for improving user engagement and advertiser return on investment.