In today’s digital landscape, the challenge of delivering accurate and satisfying recommendations to users has become increasingly complex due to the dynamic nature of user preferences and interactions. Traditional recommender systems often struggle to capture the temporal nuances and hierarchical relationships within user data. This paper introduces a novel approach that leverages Hierarchical Time-aware Graph Neural Networks (HTGNNs) to enhance the performance of graph-based recommender systems. By incorporating multi-scale temporal dynamics, HTGNNs provide a more nuanced understanding of user behavior, leading to significant improvements in recommendation accuracy and user satisfaction. Through comprehensive experiments, we demonstrate that HTGNNs outperform both traditional models and existing time-aware approaches, highlighting their potential to redefine the standards in personalized recommendation systems.