Sequential recommendation has been a widely popular topic of recommender systems. Existing works have contributed to enhancing the prediction ability of sequential recommendation systems based on various methods, such as recurrent networks and self-attention mechanisms. However, they fail to discover and distinguish various relationships between items, which could be underlying factors which motivate user behaviors. In this paper, we propose an Edge-Enhanced Global Disentangled Graph Neural Network (EGD-GNN) model to capture the relation information between items for global item representation and local user intention learning. At the global level, we build a global-link graph over all sequences to model item relationships. Then a channel-aware disentangled learning layer is designed to decompose edge information into different channels, which can be aggregated to represent the target item from its neighbors. At the local level, we apply a variational auto-encoder framework to learn user intention over the current sequence. We evaluate our proposed method on three real-world datasets. Experimental results show that our model can get a crucial improvement over state-of-the-art baselines and is able to distinguish item features.

翻译：顺序建议一直是建议者系统广为人知的主题。现有工作有助于提高基于各种方法,例如经常性网络和自省机制的顺序建议系统的预测能力。然而,它们未能发现和区分各种项目之间的关系,这可能是激发用户行为的基本因素。在本文件中,我们提议采用“Edge-Enchaned Global Discled Gima Consural Network(EGD-GNNN)”模型,以捕捉全球项目代表和地方用户意图学习项目之间的关系。在全球一级,我们为模拟项目关系的所有序列建立一个全球链接图。然后,一个注意到频道分解的学习层设计为将精锐信息分解到不同渠道,这些渠道可以聚合,从邻居那里代表目标项目。在地方一级,我们采用一个变式自动编码框架来了解用户对当前序列的意图。我们评估了三个真实世界数据集的拟议方法。实验结果显示,我们的模型可以在三个真实世界数据集上得到关键性的改进,能够区分项目特征。