Enhancing Interaction Design through User Behavior Naturalization with AI-Powered Recommendation Systems

Abstract

This study aims to explore the integration of user behavior naturalization into AI-driven recommendation systems to enhance recommendation accuracy and user experience. By incorporating user behavior modeling, recommendation strategy optimization, and dynamic interaction design, the study addresses issues such as static interfaces, recommendation mismatches, and insufficient interpretability. A mixed-methods approach was employed, using real-world datasets like MovieLens and Amazon Product Reviews to evaluate quantitative metrics such as precision, recall, click-through rate (CTR), and diversity, as well as qualitative user feedback on usability and satisfaction. The experimental results show a 15% improvement in recommendation precision, a 25% increase in CTR, and higher user engagement due to dynamic content adaptation. The findings suggest that user behavior naturalization can effectively optimize personalized recommendation systems, with significant potential applications in fields such as e-commerce, entertainment, and education.