Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs. The nucleus of existing GNN-based recommender systems involves recursive message passing along user-item interaction edges to refine encoded embeddings. Despite their demonstrated effectiveness, current GNN-based methods encounter challenges of limited receptive fields and the presence of noisy "interest-irrelevant" connections. In contrast, Transformer-based methods excel in aggregating information adaptively and globally. Nevertheless, their application to large-scale interaction graphs is hindered by inherent complexities and challenges in capturing intricate, entangled structural information. In this paper, we propose TransGNN, a novel model that integrates Transformer and GNN layers in an alternating fashion to mutually enhance their capabilities. Specifically, TransGNN leverages Transformer layers to broaden the receptive field and disentangle information aggregation from edges, which aggregates information from more relevant nodes, thereby enhancing the message passing of GNNs. Additionally, to capture graph structure information effectively, positional encoding is meticulously designed and integrated into GNN layers to encode such structural knowledge into node attributes, thus enhancing the Transformer's performance on graphs. Efficiency considerations are also alleviated by proposing the sampling of the most relevant nodes for the Transformer, along with two efficient sample update strategies to reduce complexity. Furthermore, theoretical analysis demonstrates that TransGNN offers increased expressiveness compared to GNNs, with only a marginal increase in linear complexity. Extensive experiments on five public datasets validate the effectiveness and efficiency of TransGNN.

翻译：图神经网络（GNNs）已成为通过建模用户-物品交互图来应对协同过滤（CF）挑战的有前景的解决方案。现有基于GNN的推荐系统的核心在于沿着用户-物品交互边递归传递消息，以精细化编码嵌入。尽管这些方法已展现出有效性，但当前的GNN方法仍面临感受野受限和存在与兴趣无关的噪声连接等挑战。相比之下，基于Transformer的方法在自适应和全局信息聚合方面表现出色，然而其在大型交互图上的应用受限于固有复杂性和难以捕捉错综复杂的结构信息。本文提出TransGNN，一种将Transformer层与GNN层交替集成的创新模型，以实现两者能力的相互增强。具体而言，TransGNN利用Transformer层扩大感受野并将信息聚合与边缘解耦，通过从更相关的节点聚合信息，进而增强GNN的消息传递能力。此外，为有效捕捉图结构信息，我们精心设计了位置编码并将其融入GNN层，将结构知识编码为节点属性，从而提升Transformer在图上的表现。通过提出为Transformer采样最相关节点，并采用两种高效的样本更新策略以降低复杂度，本文也缓解了效率问题。进一步的理论分析表明，与GNN相比，TransGNN在仅增加线性复杂度的前提下具有更强的表达能力。在五个公开数据集上的大量实验验证了TransGNN的有效性和高效性。