Metapath-guided multi-headed attention networks for trust prediction in heterogeneous social networks
Metapath-guided multi-headed attention networks for trust prediction in heterogeneous social networks
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DOI：
10.1016/j.knosys.2023.111119
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发表时间：
2023-12
期刊：
影响因子：
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通讯作者：
Yanwei Xu;Zhiyong Feng;Meng Xing;Hongyue Wu;Shizhan Chen;Xiao Xue;S. Dustdar
中科院分区：
文献类型：
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作者：
Yanwei Xu;Zhiyong Feng;Meng Xing;Hongyue Wu;Shizhan Chen;Xiao Xue;S. Dustdar
信任预测促进了各种基于网络的应用程序的日常功能，例如推荐系统、市场广告和异常检测。然而，现有的工作严重依赖于用户与用户之间的信任交互，这导致由于数据稀疏而导致性能有限。先前的研究表明，用户之间的信任关系受到用户交互的项目类别的显着影响。在本文中，我们提出了一种 MetaTrust 模型，该模型生成冗余的用户-项目交互作为用户-用户信任的补充，以减轻信任预测的数据稀疏性。具体来说，我们提出了类别感知元路径，它根据用户交互的常见项目类别生成丰富的用户-项目-用户交互。此外，长短期记忆（LSTM）网络用于挖掘多个类别感知元路径的特征及其相关性。为了过滤与当前任务无关的用户-项目-用户交互，用MLP将用户之间的真实信任关系嵌入到网络中。最后，利用多头注意力网络来区分哪个元路径决定当前用户对之间的信任预测。对三个真实世界数据集的广泛实验表明，我们提出的模型可以有效地实现比其他竞争方法显着的改进，并显示信任建立的潜在可解释性。
Trust prediction facilitates the day-to-day functionality of diverse web-based applications, such as recommendation systems, market advertising and anomaly detection. However, existing works heavily rely on user–user trust interactions, which result in limited performance as the data sparsity. Previous studies have shown that the trust relationship between users is significantly affected by the category of items that the users interacted. In this paper, we propose a MetaTrust model, which generates redundant user-item interactions as the supplement of user–user trust to alleviate the data sparsity on trust prediction. Specifically, we propose category-aware metapaths, which generate abundant user–item–user interactions based on the common item category that users have interacted with. Further, Long Short Term Memory (LSTM) networks are utilized to mine features of multiple category-aware metapaths and their correlations. In order to filter the user–item–user interactions that are not related to the current task, the real trust relationship between users are embedd in the network with MLP. Finally, a multi-headed attention network is utilized to distinguish which metapath determines trust prediction between the current pair of users. Extensive experiments on three real-world dataset show that our proposed model can effectively achieve significant improvements over other competitive approaches and show the potential interpretability of trust building.