Session-based recommendation systems suggest relevant items to users by modeling user behavior and preferences using short-term anonymous sessions. Existing methods leverage Graph Neural Networks (GNNs) that propagate and aggregate information from neighboring nodes i.e., local message passing. Such graph-based architectures have representational limits, as a single sub-graph is susceptible to overfit the sequential dependencies instead of accounting for complex transitions between items in different sessions. We propose using a Transformer in combination with a target attentive GNN, which allows richer Representation Learning. Our experimental results and ablation show that our proposed method outperforms the existing methods on real-world benchmark datasets.

中文翻译：

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基于会话的推荐的改进表示学习

基于会话的推荐系统通过使用短期匿名会话对用户行为和偏好进行建模，向用户建议相关项目。现有方法利用图神经网络 (GNN)，从相邻节点传播和聚合信息，即本地消息传递。这种基于图的架构具有表示限制，因为单个子图容易过度拟合顺序依赖性，而不是考虑不同会话中项目之间的复杂转换。我们建议将 Transformer 与目标注意力集中的 GNN 结合使用，从而实现更丰富的表征学习。我们的实验结果和消融表明，我们提出的方法优于现实世界基准数据集上的现有方法。