Knowledge graphs (KGs) have proven to be effective for highquality recommendation. However, existing methods mainly investigate separate paths connecting user-item pairs from KGs, thus failing to fully capture the rich semantics and underlying topology of KGs. We, therefore, propose a novel attentive knowledge graph embedding (AKGE) framwork to exploit the complex subgraphs of KGs linking user-item pairs to help better infer user preference. Specifically, AKGE first employs a distance-aware sampling strategy to automatically extract high-order subgraphs, which represent user-item relations with rich semantics. The subgraphs are then encoded by the proposed attentive graph neural network to help learn accurate user preference over items. Extensive validation shows that AKGE consistently outperforms state-of-the-arts. It additionally provides potential explanations for recommendation results.
翻译:事实证明,知识图(KGs)对于高质量的建议是有效的。但是,现有方法主要是调查连接KGs用户项目配对的不同路径,从而未能充分捕捉到KGs丰富的语义学和基本地形学。因此,我们提出一个新的关注知识图嵌入框架(AKGE),以利用KGs复杂的分层图将用户项目配对连接起来,帮助更好地推断用户的偏好。具体地说,AKGE首先采用远程抽样战略,自动提取高档子图,它代表用户项目与丰富的语义学的关系。这些子图随后由拟议的专注图形神经网络编码,以帮助了解用户对项目的准确偏好。广泛的验证表明AGE公司一贯优于状态。它还为建议结果提供了潜在的解释。