Session-based recommendation, without the access to a user’s historical user-item interactions, is a challenging task, where the available information in an ongoing session is very limited. Previous work on session-based recommendation has considered sequences of items that users have interacted with sequentially. Such item sequences may not fully capture the complex transition relationship between items that go beyond the inspection order. This issue is partially addressed by the graph neural network (GNN) based models. However, GNNs can only propagate information from adjacent items while neglecting items without a direct connection, which makes the latent connections unavailable in propagation of GNNs. Importantly, GNN-based approaches often face a serious overfitting problem. Thus, we propose Star Graph Neural Networks with Highway Net- works (SGNN-HN) for session-based recommendation. The proposed SGNN-HN model applies a star graph neural network (SGNN) to model the complex transition relationship between items in an ongoing session. To avoid overfitting, we employ the highway networks (HN) to adaptively select embeddings from item representations before and after multi-layer SGNNs. Finally, we aggregate the item embeddings generated by SGNN in an ongoing session to represent a user’s final preference for item prediction. Experiments are conducted on two public benchmark datasets, i.e., Yoochoose and Diginetica. The results show that SGNN-HN can outperform the state-of-the-art models in terms of Recall and MRR for session-based recommendation.

基于会话的推荐，无需访问用户的历史用户项目交互，是一项具有挑战性的任务，其中正在进行的会话中的可用信息非常有限。以前关于基于会话的推荐的工作已经考虑了用户顺序交互的项目序列。这样的项目序列可能无法完全捕捉到超出检验顺序的项目之间的复杂过渡关系。基于图神经网络 (GNN) 的模型部分解决了这个问题。然而，GNNs 只能传播来自相邻项目的信息，而忽略没有直接连接的项目，这使得潜在连接在 GNN 的传播中不可用。重要的是，基于 GNN 的方法经常面临严重的过拟合问题。因此，我们提出了带有高速公路网络的星图神经网络（SGNN-HN）用于基于会话的推荐。所提出的 SGNN-HN 模型应用星图神经网络 (SGNN) 来模拟正在进行的会话中项目之间的复杂转换关系。为了避免过度拟合，我们使用高速网络 (HN) 在多层 SGNN 之前和之后从项目表示中自适应地选择嵌入。最后，我们在正在进行的会话中聚合 SGNN 生成的项目嵌入，以表示用户对项目预测的最终偏好。实验在两个公共基准数据集上进行，即 我们使用高速公路网络（HN）在多层 SGNN 之前和之后从项目表示中自适应地选择嵌入。最后，我们在正在进行的会话中聚合 SGNN 生成的项目嵌入，以表示用户对项目预测的最终偏好。实验在两个公共基准数据集上进行，即 我们使用高速公路网络（HN）在多层 SGNN 之前和之后从项目表示中自适应地选择嵌入。最后，我们在正在进行的会话中聚合 SGNN 生成的项目嵌入，以表示用户对项目预测的最终偏好。实验在两个公共基准数据集上进行，即Yoochoose和Diginetica。结果表明，SGNN-HN 在基于会话的推荐的召回和 MRR 方面可以优于最先进的模型。