论文标题
推荐系统的协作感知图卷积网络
Collaboration-Aware Graph Convolutional Network for Recommender Systems
论文作者
论文摘要
图形神经网络（GNN）已通过隐式捕获协作效应的消息来成功地在推荐系统中成功采用。然而，大多数现有的消息通讯机制直接从GNN继承而来，而无需仔细检查捕获的协作效果是否会受益于用户偏好的预测。在本文中，我们首先分析了消息传播如何捕获协作效应，并提出了面向建议的拓扑指标，共同的相互作用比率（CIR），该比例（CIR）测量了节点的特定邻居与其其余邻居之间的相互作用水平。在证明了利用邻居与较高CIR的合作的好处之后，我们提出了一个推荐销售的GNN，协作感知的图形卷积网络（CAGCN），该网络（CAGCN）超出了1- weisfeiler-lehman（1-WL）测试，以区分非竞争对手的材料 - 材料 - 材料 - 词素 - 异构图形。六个基准数据集的实验表明，最佳CAGCN变体的表现优于最具代表性的基于GNN的推荐模型LightGCN，在召回@20中的表现近10％，并且达到了80％的速度约为80％。我们的代码可在https://github.com/yuwvandy/cagcn上公开获取。
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.
