Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is to incorporate rich semantic information for more accurate recommendations. However, two main challenges persist: i) Neglecting complex higher-order interactions in the KG-based user-item network, potentially leading to sub-optimal recommendations, and ii) Dealing with the heterogeneous modalities of input sources, such as user-item bipartite graphs and KGs, which may introduce noise and inaccuracies. To address these issues, we present a novel Knowledge-enhanced Heterogeneous Hypergraph Recommender System (KHGRec). KHGRec captures group-wise characteristics of both the interaction network and the KG, modeling complex connections in the KG. Using a collaborative knowledge heterogeneous hypergraph (CKHG), it employs two hypergraph encoders to model group-wise interdependencies and ensure explainability. Additionally, it fuses signals from the input graphs with cross-view self-supervised learning and attention mechanisms. Extensive experiments on four real-world datasets show our model's superiority over various state-of-the-art baselines, with an average 5.18\% relative improvement. Additional tests on noise resilience, missing data, and cold-start problems demonstrate the robustness of our KHGRec framework. Our model and evaluation datasets are publicly available at \url{https://github.com/viethungvu1998/KHGRec}.

翻译：近年来，推荐系统的研究进展集中于整合知识图谱以利用其辅助信息。基于知识图谱增强的推荐系统的核心思想是融入丰富的语义信息以实现更精准的推荐。然而，两大挑战依然存在：i) 忽略了基于知识图谱的用户-物品网络中复杂的高阶交互，可能导致次优推荐；ii) 处理输入源的异质模态（如用户-物品二部图和知识图谱）可能引入噪声与不准确性。为解决这些问题，本文提出了一种新颖的知识增强异质超图推荐系统（KHGRec）。KHGRec 通过协作知识异质超图（CKHG）同时捕捉交互网络与知识图谱的群体特征，对知识图谱中的复杂连接进行建模。该系统采用两个超图编码器来建模群体间的相互依赖关系并确保可解释性。此外，它通过跨视图自监督学习与注意力机制融合来自输入图的信号。在四个真实世界数据集上的大量实验表明，我们的模型优于多种先进基线方法，平均相对提升达5.18%。在噪声鲁棒性、数据缺失和冷启动问题上的额外测试验证了KHGRec框架的稳健性。我们的模型与评估数据集已公开于 \url{https://github.com/viethungvu1998/KHGRec}。