Knowledge graphs (KGs) have proven to be effective for high-quality recommendation, where the connectivities between users and items provide rich and complementary information to user-item interactions. Most existing methods, however, are insufficient to exploit the KGs for capturing user preferences, as they either represent the user-item connectivities via paths with limited expressiveness or implicitly model them by propagating information over the entire KG with inevitable noise. In this paper, we design a novel hierarchical attentive knowledge graph embedding (HAKG) framework to exploit the KGs for effective recommendation. Specifically, HAKG first extracts the expressive subgraphs that link user-item pairs to characterize their connectivities, which accommodate both the semantics and topology of KGs. The subgraphs are then encoded via a hierarchical attentive subgraph encoding to generate effective subgraph embeddings for enhanced user preference prediction. Extensive experiments show the superiority of HAKG against state-of-the-art recommendation methods, as well as its potential in alleviating the data sparsity issue.

知识图谱 (KG) 已被证明对于高质量推荐是有效的，其中用户和项目之间的连接为用户-项目交互提供了丰富的补充信息。然而，大多数现有方法不足以利用 KG 来捕获用户偏好，因为它们要么通过表达能力有限的路径表示用户-项目连接，要么通过在整个 KG 上传播信息并带有不可避免的噪声来隐式建模它们。在本文中，我们设计了一种新颖的分层注意力知识图嵌入（HAKG）框架来利用 KG 进行有效推荐。具体来说，HAKG 首先提取链接用户-项目对的表达子图来表征它们的连接性，这同时适应了 KG 的语义和拓扑。然后通过分层注意子图编码对子图进行编码，以生成有效的子图嵌入，以增强用户偏好预测。大量实验表明 HAKG 相对于最先进的推荐方法的优越性，以及它在缓解数据稀疏问题方面的潜力。