论文标题
分层bigraph神经网络作为推荐系统
Hierarchical BiGraph Neural Network as Recommendation Systems
论文作者
论文摘要
图形神经网络是一种有前途的建模方法，用于处理图形域中最能表示的数据集。在特定的，开发的建议系统中，通常需要解决稀疏的结构化数据，这些数据通常缺乏用户和/或项目方面的特征丰富性，并且需要在正确的上下文中处理以获得最佳性能。这些数据集可以直观地映射到并表示为网络或图形。在本文中，我们提出了分层Bigraph神经网络（HBGNN），这是一种将GNN用作建议系统并使用BigRaph Framework构建用户项目功能的层次结构方法。我们的实验结果显示了具有当前推荐系统方法和可传递性的竞争性能。
Graph neural networks emerge as a promising modeling method for applications dealing with datasets that are best represented in the graph domain. In specific, developing recommendation systems often require addressing sparse structured data which often lacks the feature richness in either the user and/or item side and requires processing within the correct context for optimal performance. These datasets intuitively can be mapped to and represented as networks or graphs. In this paper, we propose the Hierarchical BiGraph Neural Network (HBGNN), a hierarchical approach of using GNNs as recommendation systems and structuring the user-item features using a bigraph framework. Our experimental results show competitive performance with current recommendation system methods and transferability.
