On the Performance of Artificial Intelligence Empowerment on Consumer Behavior

In recent years, the rapid advancement of artificial intelligence (AI) technology has provided substantial impetus for the innovation and optimization of personalized recommendation systems. However, research on the correlation between personalized recommender systems and consumer behavior remains relatively limited, with a lack of systematic theoretical exploration and empirical analysis. This study employs a questionnaire survey method to collect detailed data samples and innovatively integrates heat map analysis technology to conduct a comprehensive investigation into the relationship between personalized recommendation systems and consumer behavior evaluation variables. The study focuses on elucidating the mechanisms through which personalized recommender systems influence consumer preferences, purchase decisions, and usage satisfaction, aiming to uncover their multi-level and multi-dimensional impact pathways. The data analysis reveals that personalized recommendation systems significantly affect multiple key dimensions of consumer behavior, demonstrating a high degree of relevance and interactivity. These findings provide crucial theoretical support for a deeper understanding of the operational principles and optimization strategies of personalized recommendation systems, while also offering a new practical perspective for interdisciplinary research at the intersection of AI technology and consumer behavior, thereby holding significant academic and practical value.