The dynamic nature of user preferences poses significant challenges for recommender systems, necessitating models that can effectively capture both short-term fluctuations and long-term behavioral patterns. Traditional graph-based approaches and existing time-aware models often fail to fully integrate hierarchical temporal dependencies, leading to suboptimal recommendation performance. In this paper, we propose a novel Hierarchical Time-aware Graph Neural Network (HTGNN) framework that incorporates multi-scale temporal dynamics to enhance recommendation accuracy. HTGNN constructs a hierarchical representation of user-item interactions over time, enabling it to model evolving preferences with greater granularity. Our experimental results across multiple benchmark datasets demonstrate that HTGNN consistently outperforms state-of-the-art models, achieving superior ranking quality and user satisfaction. These findings underscore the effectiveness of HTGNN in advancing personalized recommendation systems by bridging the gap between temporal modeling and hierarchical learning.
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