arXiv 2407.036652024-07-04cs.IRcs.AIcs.LGcs.SIstat.ML

面向推荐系统的异构超图嵌入

Heterogeneous Hypergraph Embedding for Recommendation Systems

Griffith University（格里菲斯大学）；Hanoi University of Science and Technology（河内科技大学）；Ecole Polytechnique Federale de Lausanne（洛桑联邦理工学院）；The University of Queensland（昆士兰大学）

Darnbi Sakong, Viet Hung Vu, Thanh Trung Huynh, Phi Le Nguyen, Hongzhi Yin, Quoc Viet Hung Nguyen, Thanh Tam Nguyen

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AI总结：

本文提出知识增强异构超图推荐系统KHGRec，通过协作知识异构超图建模高阶交互，结合跨视图自监督学习与注意力机制，在多个数据集上平均提升5.18%，并验证了噪声鲁棒性和冷启动性能。

摘要：

推荐系统的最新进展集中在整合知识图谱（KGs）以利用其辅助信息。知识图谱增强型推荐器的核心思想是引入丰富的语义信息以实现更准确的推荐。然而，两个主要挑战仍然存在：i）忽略了基于知识图谱的用户-物品网络中的复杂高阶交互，可能导致次优推荐；ii）处理输入源的异构模态，如用户-物品二部图和知识图谱，可能引入噪声和不准确性。为了解决这些问题，我们提出了一种新颖的知识增强异构超图推荐系统（KHGRec）。KHGRec捕获交互网络和知识图谱的群体特征，对知识图谱中的复杂连接进行建模。利用协作知识异构超图（CKHG），它采用两个超图编码器来建模群体级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和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，我们的模型优于各种最先进的基线，平均相对提升5.18%。对噪声鲁棒性、缺失数据和冷启动问题的额外测试证明了我们KHGRec框架的鲁棒性。我们的模型和评估数据集在\url{https://github.com/viethungvu1998/KHGRec}上公开可用。

英文摘要：

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}.

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