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（GNN）通过建模用户-商品交互图，已成为协同过滤（CF）中极具前景的解决方案。现有基于GNN的推荐系统的核心在于沿用户-商品交互边进行递归消息传递以精炼编码嵌入。尽管已展现出显著效果，但当前基于GNN的方法仍面临感受野受限和存在噪声“兴趣无关”连接的挑战。相比之下，基于Transformer的方法擅长自适应全局信息聚合，但其在大规模交互图上的应用受限于固有复杂性和对复杂纠缠结构信息捕获的困难。本文提出TransGNN——一种交替集成Transformer层与GNN层的新型模型，使二者能力相互增强。具体而言，TransGNN利用Transformer层拓宽感受野并将信息聚合与边解耦，从而从更相关的节点聚合信息，增强GNN的消息传递。为有效捕获图结构信息，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，进而提升Transformer在图上表现。通过提出为Transformer采样最相关节点及两种高效样本更新策略以降低复杂度，效率问题也得到缓解。理论分析表明，TransGNN相比GNN具有更强的表达能力，且仅带来线性复杂度的微小增加。在五个公开数据集上的大量实验验证了TransGNN的有效性与效率。