Graph Neural Networks (GNNs) have been successfully adopted in recommender systems by virtue of the message-passing that implicitly captures collaborative effect. Nevertheless, most of the existing message-passing mechanisms for recommendation are directly inherited from GNNs without scrutinizing whether the captured collaborative effect would benefit the prediction of user preferences. In this paper, we first analyze how message-passing captures the collaborative effect and propose a recommendation-oriented topological metric, Common Interacted Ratio (CIR), which measures the level of interaction between a specific neighbor of a node with the rest of its neighbors. After demonstrating the benefits of leveraging collaborations from neighbors with higher CIR, we propose a recommendation-tailored GNN, Collaboration-Aware Graph Convolutional Network (CAGCN), that goes beyond 1-Weisfeiler-Lehman(1-WL) test in distinguishing non-bipartite-subgraph-isomorphic graphs. Experiments on six benchmark datasets show that the best CAGCN variant outperforms the most representative GNN-based recommendation model, LightGCN, by nearly 10% in Recall@20 and also achieves around 80% speedup. Our code is publicly available at https://github.com/YuWVandy/CAGCN.

翻译：图神经网络（GNN）凭借隐式捕捉协作效应的消息传递机制，已在推荐系统中得到成功应用。然而，现有面向推荐的消息传递机制大多直接继承自GNN，未深入探究所捕捉的协作效应是否真正有利于用户偏好预测。本文首先分析了消息传递如何捕捉协作效应，并提出了一种面向推荐的拓扑度量指标——共同交互比（CIR），用于衡量节点特定邻居与其余邻居间的交互程度。在验证利用高CIR邻居的协作优势后，我们提出了一种推荐定制的图神经网络——协作感知图卷积网络（CAGCN），该模型在区分非二分图同构子图方面超越了1-Weisfeiler-Lehman（1-WL）测试。在六个基准数据集上的实验表明，最佳CAGCN变体在Recall@20指标上比最具代表性的基于GNN的推荐模型LightGCN提升近10%，同时实现约80%的加速。我们的代码已开源在https://github.com/YuWVandy/CAGCN。