Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF) models. One classical approach in GCF is to learn user and item embeddings by modeling complex graph relations and utilizing these embeddings for CF models. However, the quality of the embeddings significantly impacts the recommendation performance of GCF models. In this paper, we argue that existing graph learning methods are insufficient in generating satisfactory embeddings for CF models. This is because they aggregate neighboring node messages directly, which can result in incorrect estimations of user-item correlations. To overcome this limitation, we propose a novel approach that incorporates causal modeling to explicitly encode the causal effects of neighboring nodes on the target node. This approach enables us to identify spurious correlations and uncover the root causes of user preferences. We introduce Causal Neural Graph Collaborative Filtering (CNGCF), the first causality-aware graph learning framework for CF. CNGCF integrates causal modeling into the graph representation learning process, explicitly coupling causal effects between node pairs into the core message-passing process of graph learning. As a result, CNGCF yields causality-aware embeddings that promote robust recommendations. Our extensive experiments demonstrate that CNGCF provides precise recommendations that align with user preferences. Therefore, our proposed framework can address the limitations of existing GCF models and offer a more effective solution for recommendation systems.

翻译：图协同过滤（GCF）通过利用图学习技术增强协同过滤（CF）模型，已在推荐系统中获得广泛关注。GCF的一种经典方法是通过建模复杂图关系来学习用户和物品的嵌入表示，并将这些嵌入用于CF模型。然而，嵌入质量显著影响GCF模型的推荐性能。本文指出，现有图学习方法难以生成满足CF模型要求的理想嵌入，原因在于它们直接聚合相邻节点的消息，可能导致用户-物品相关性的错误估计。为克服这一局限，我们提出了一种融合因果建模的新方法，显式编码相邻节点对目标节点的因果效应。该方法能够识别虚假相关性并揭示用户偏好的根本原因。我们首次提出了因果感知图学习框架——因果神经图协同过滤（CNGCF），将因果建模集成到图表示学习过程中，显式地将节点对间的因果效应耦合至图学习的核心消息传递流程中。由此，CNGCF生成了促进鲁棒推荐的因果感知嵌入。大量实验表明，CNGCF能够提供与用户偏好高度一致的精准推荐。因此，所提框架可解决现有GCF模型的局限性，为推荐系统提供更有效的解决方案。