Knowledge graphs (KGs) are commonly used as side information to enhance collaborative signals and improve recommendation quality. In the context of knowledge-aware recommendation (KGR), graph neural networks (GNNs) have emerged as promising solutions for modeling factual and semantic information in KGs. However, the long-tail distribution of entities leads to sparsity in supervision signals, which weakens the quality of item representation when utilizing KG enhancement. Additionally, the binary relation representation of KGs simplifies hyper-relational facts, making it challenging to model complex real-world information. Furthermore, the over-smoothing phenomenon results in indistinguishable representations and information loss. To address these challenges, we propose the SDK (Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph) framework. This framework establishes a cross-view hypergraph self-supervised learning mechanism for KG enhancement. Specifically, we model hyper-relational facts in KGs to capture interdependencies between entities under complete semantic conditions. With the refined representation, a hypergraph is dynamically constructed to preserve features in the deep vector space, thereby alleviating the over-smoothing problem. Furthermore, we mine external supervision signals from both the global perspective of the hypergraph and the local perspective of collaborative filtering (CF) to guide the model prediction process. Extensive experiments conducted on different datasets demonstrate the superiority of the SDK framework over state-of-the-art models. The results showcase its ability to alleviate the effects of over-smoothing and supervision signal sparsity.

翻译：知识图谱（KGs）常作为辅助信息用于增强协同信号并提升推荐质量。在知识感知推荐（KGR）场景中，图神经网络（GNNs）已成为对知识图谱中的事实与语义信息进行建模的有效方案。然而，实体长尾分布导致监督信号稀疏，削弱了利用知识图谱增强时项目表示的质量。此外，知识图谱的二元关系表示简化了超关系事实，使得对复杂现实世界信息的建模面临挑战。同时，过度平滑现象导致表示难以区分并造成信息损失。为解决上述问题，我们提出SDK（基于超关系知识图谱的自监督动态超图推荐）框架。该框架建立了一种跨视图超图自监督学习机制用于知识图谱增强。具体而言，我们对知识图谱中的超关系事实进行建模，以在完整语义条件下捕获实体间的相互依赖关系。基于优化的表示，我们动态构建超图以保留深层向量空间中的特征，从而缓解过度平滑问题。此外，我们从超图的全局视角和协同过滤（CF）的局部视角挖掘外部监督信号，以指导模型预测过程。在多个数据集上进行的广泛实验表明，SDK框架相较于现有最优模型具有优越性。实验结果展示了其在缓解过度平滑与监督信号稀疏影响方面的能力。