In graph neural networks (GNNs), message passing iteratively aggregates nodes' information from their direct neighbors while neglecting the sequential nature of multi-hop node connections. Such sequential node connections e.g., metapaths, capture critical insights for downstream tasks. Concretely, in recommender systems (RSs), disregarding these insights leads to inadequate distillation of collaborative signals. In this paper, we employ collaborative subgraphs (CSGs) and metapaths to form metapath-aware subgraphs, which explicitly capture sequential semantics in graph structures. We propose meta\textbf{P}ath and \textbf{E}ntity-\textbf{A}ware \textbf{G}raph \textbf{N}eural \textbf{N}etwork (PEAGNN), which trains multilayer GNNs to perform metapath-aware information aggregation on such subgraphs. This aggregated information from different metapaths is then fused using attention mechanism. Finally, PEAGNN gives us the representations for node and subgraph, which can be used to train MLP for predicting score for target user-item pairs. To leverage the local structure of CSGs, we present entity-awareness that acts as a contrastive regularizer on node embedding. Moreover, PEAGNN can be combined with prominent layers such as GAT, GCN and GraphSage. Our empirical evaluation shows that our proposed technique outperforms competitive baselines on several datasets for recommendation tasks. Further analysis demonstrates that PEAGNN also learns meaningful metapath combinations from a given set of metapaths.

翻译：在图形神经网络(GNNs)中,信息通过迭代式汇总节点从直接邻居传递的信息,而忽略了多霍节点连接的相继性质。这些相继节点连接,例如元路径,为下游任务获取关键洞见。具体地说,在建议系统(RSs)中,无视这些洞见,导致协作信号的蒸馏不足。在本文中,我们使用协作子集和元路径来形成正反正觉子集集,这些分集在图形结构中明确显示序列语义。我们提议了mextbf{P}ath 和\ textbf{E}textbf{T{A}}A}关于连续节点节点连接的相继性连接。这种相继节点连接连接,我们提议了textb{Gtextf{G}Grupf}Netroupform, PEGNGNGNations 也向我们展示了一种常规的演示方式,用来将GSqual-al Adal Adal Adal 这样的动作作为我们的系统。