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.
翻译:基于会议的建议系统通过模拟用户行为和偏好,使用短期匿名会议,向用户建议相关项目。现有方法利用地理神经网络(GNN)来传播和汇总来自邻近节点的信息,即本地信息传递。这种基于图形的架构有代表性限制,因为单一子集有可能过度适应相继依赖,而不是在不同会议中计算项目之间的复杂过渡。我们提议使用变换器,结合目标注意的GNN(GNN),使更富于代表性学习。我们的实验结果和对比显示,我们提出的方法超过了现实世界基准数据集的现有方法。