Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Dynamic user preference modeling in graph-based recommender systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月29日

查看全文
  • Rania Abidi,
  • Wissem Inoubli,
  • Mouhamed Ghaith Ayadi,
  • Wahiba Ben Abdessalem

摘要

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.