X-2ch: Quad-Channel Collaborative Graph Network over Knowledge-Embedded Edges
X-2ch: Quad-Channel Collaborative Graph Network over Knowledge-Embedded Edges
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DOI：
10.1145/3404835.3463003
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发表时间：
2021-07
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影响因子：
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通讯作者：
Kachun Lo;Tsukasa Ishigaki
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文献类型：
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作者：
Kachun Lo;Tsukasa Ishigaki
知识图（KG）承载着丰富的边信息，在丰富协同过滤（CF）的稀疏性方面显示出巨大的潜力。尽管图神经网络（GNN）已经成功地用于同时从KG和CF信号学习用户偏好，但是大多数模型由于其缺陷设计而遭受较差的性能，即，1)在用户、项目和KG实体之间不进行区分，2）将KG信号与CF信号混淆，以及3）完全忽略边缘的影响，这对于图信息传播是至关重要的。在本文中，我们提出了一个四通道图模型（X-2ch）来解决这些问题。首先，X-2ch不是将KG实体作为节点存放在图上，而是提取KG信息并以双向方式将其嵌入为边属性，以模拟自然的用户-项目交互过程。其次，X-2ch引入了一种新颖的四通道学习方案，包括协作用户-项目更新和CF-KG注意传播，以全面捕获用户和项目的互连性，同时保留其独特的属性。在两个真实世界的基准测试上的实验表明，与最先进的基线相比，该方法有了很大的改进。
Carrying abundant side information, knowledge graph (KG) has shown its great potential in enriching the sparsity of collaborative filtering (CF) for recommendation. Although graph neural networks (GNNs) have been successfully employed to learn user preferences from KG and CF signals simultaneously, most models suffer from inferior performance due to their deficient designs, i.e., 1) formulating no distinction between users, items and KG entities, 2) confounding KG signals with CF signals and 3) completely neglecting the effects of edges, which is vital for graph information propagation. In this paper, we propose a quad-channel graph model (X-2ch) to tackle these problems. First, rather than lodging KG entities on graph as nodes, X-2ch distills KG information and embeds them as edge attributes in a bi-directional manner to model the natural user-item interaction process. Second, X-2ch introduces a novel quad-channel learning scheme, including a collaborative user-item update and a CF-KG attentive propagation, to holistically capture the interconnectivity of users and items while preserving their distinct properties. Experiments on two real-world benchmarks show substantial improvement over the state-of-the-art baselines.