Session-based recommendations which predict the next action by understanding a user's interaction behavior with items within a relatively short ongoing session have recently gained increasing popularity. Previous research has focused on capturing the dynamics of sequential dependencies from complicated item transitions in a session by means of recurrent neural networks, self-attention models, and recently, mostly graph neural networks. Despite the plethora of different models relying on the order of items in a session, few approaches have been proposed for dealing better with the temporal implications between interactions. We present Temporal Graph Neural Networks (TempGNN), a generic framework for capturing the structural and temporal dynamics in complex item transitions utilizing temporal embedding operators on nodes and edges on dynamic session graphs, represented as sequences of timed events. Extensive experimental results show the effectiveness and adaptability of the proposed method by plugging it into existing state-of-the-art models. Finally, TempGNN achieved state-of-the-art performance on two real-world e-commerce datasets.

翻译：会话推荐通过理解用户在较短持续会话中与物品的交互行为来预测下一步操作，近年来受到广泛关注。先前研究主要集中于利用循环神经网络、自注意力模型以及最近广泛使用的图神经网络，从会话中复杂物品转换中捕捉序列依赖的动态特性。尽管存在大量依赖于会话中物品顺序的模型，但针对更好处理交互间时间关联性的方法仍相对匮乏。我们提出了时序图神经网络（TempGNN）这一通用框架，通过在动态会话图的节点和边上应用时序嵌入算子（该会话图表现为带时间戳事件序列），来捕捉复杂物品转换中的结构与时序动态特性。大量实验结果表明，将所提方法嵌入现有最优模型后，其有效性与适应性得到了验证。最终，TempGNN在两个真实电商数据集上取得了最先进性能。