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<p>Social recommendation leverages users’ social connections to enhance recommendation accuracy. However, user behavior and interests evolve over time, posing challenges for traditional social models that fail to dynamically capture these changes. As a result, existing approaches struggle to reflect users’ current preferences, leading to suboptimal recommendation performance. To address this limitation, this paper proposes a graph neural network with dynamic similarity fusion (termed GNN–DSF) for social recommendation. GNN–DSF reconstructs the social relationship graph by extracting user similarity from both the user-item interaction graph and the social graph, enabling a more adaptive and comprehensive representation of user relationships. Furthermore, we integrate dynamic and static representations of users and items, capturing both temporal variations and long-term dependencies to enhance the personalization of recommendations. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach in improving recommendation accuracy compared to existing methods.</p>

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Graph neural networks with dynamic similarity fusion for social recommendation

  • 期刊论文

  • 发表日期：

    2026年1月17日

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  • Yongchun Gu,
  • Shuangshuang Ding,
  • Yongqi Li,
  • Huiting Wang,
  • Ming Li

摘要

Social recommendation leverages users’ social connections to enhance recommendation accuracy. However, user behavior and interests evolve over time, posing challenges for traditional social models that fail to dynamically capture these changes. As a result, existing approaches struggle to reflect users’ current preferences, leading to suboptimal recommendation performance. To address this limitation, this paper proposes a graph neural network with dynamic similarity fusion (termed GNN–DSF) for social recommendation. GNN–DSF reconstructs the social relationship graph by extracting user similarity from both the user-item interaction graph and the social graph, enabling a more adaptive and comprehensive representation of user relationships. Furthermore, we integrate dynamic and static representations of users and items, capturing both temporal variations and long-term dependencies to enhance the personalization of recommendations. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach in improving recommendation accuracy compared to existing methods.