Abstract: Hypergraph Neural Networks (HGNNs) have emerged as a prominent research direction in recommender systems due to their capability to model high-order interactions and integrate multi-source heterogeneous information. Unlike traditional Graph Neural Networks (GNNs), which are limited to pairwise relationships, HGNNs employ hyperedges to capture high-order associations among an arbitrary number of nodes, thereby preserving complex semantics in user–item interactions, such as many-to-many relationships, group structures, and multimodal information.This paper first outlines the general pipeline of HGNN-based recommendation from four aspects: data input, hypergraph construction, representation learning, and recommendation generation. Furthermore, recent advances in HGNN-based recommendation are systematically reviewed from two perspectives: hypergraph construction strategies and feature propagation mechanisms. These developments are analyzed across multiple application scenarios, including sequential recommendation, multi-behavior recommendation, social recommendation, multimodal recommendation, and group recommendation.In the context of sequential recommendation, existing studies have explored various hypergraph construction strategies, including local dependency modeling based on session interactions, global co-occurrence modeling, cross-session collaborative modeling, and multi-scale spatiotemporal dynamic modeling. Correspondingly, feature propagation mechanisms that integrate hypergraph attention-based denoising and self-supervised contrastive learning have been investigated to enhance temporal representation learning. These approaches help overcome the “neighborhood limitation” inherent in conventional GNNs and enable more accurate modeling of users’ evolving interests and long-range dependencies.For multi-behavior recommendation, hypergraph construction strategies are categorized into behavior-specific modeling, unified behavior modeling, and temporal behavior modeling. Feature propagation mechanisms, such as cascaded dependency propagation, behavior-aware attention, and cross-view contrastive learning-based denoising, have been developed to address data sparsity in target behaviors, facilitate semantic alignment across behaviors, and support knowledge transfer.In social recommendation, existing works focus on hypergraph construction methods based on homophily-driven dual views, heterogeneous semantic relationships, and privacy-preserving mechanisms. Feature propagation strategies incorporating trust-aware attention and dual-channel gated fusion have been proposed, which extend beyond traditional pairwise social modeling and contribute to capturing complex group influence and high-order social structures.For multimodal recommendation, hypergraph construction strategies include modality-specific separation, collaborative semantic association, and multimodal hypergraph optimization. Feature propagation mechanisms based on modality-specific convolutional aggregation and cross-modal contrastive alignment have demonstrated effectiveness in reducing modality noise and enabling high-order reasoning within a unified semantic space, thereby improving representation quality.In group recommendation, hypergraph construction approaches involve multi-view hierarchical alignment, group structure-aware optimization, and tripartite relationship modeling. Feature propagation mechanisms that incorporate cross-level feedback and attention-based aggregation better align with the inherent “inclusion” relationships within groups and provide an effective solution for alleviating cold-start issues in dynamic group scenarios. Despite these advancements, several challenges remain in HGNN-based recommendation. Dynamic hypergraph models often face difficulties in meeting the requirements of real-time recommendation. High-order aggregation may introduce information resolution loss, while noisy pseudo-hyperedges can adversely affect model robustness. In addition, the computational and storage complexity of hypergraphs poses scalability challenges in large-scale applications. Furthermore, issues related to interpretability and fairness in recommendation results remain insufficiently addressed.To address these challenges, this paper discusses several promising research directions for future HGNN-based recommendation systems, including representation learning based on generative self-supervised disentanglement, lightweight and efficient training and inference frameworks, robustness enhancement via causal inference, scenario-aware multimodal fusion, and collaborative integration with large language models. These directions are expected to provide valuable insights for advancing research in this field.

摘要： 超图神经网络（Hypergraph Neural Network, HGNN）因其在建模高阶交互与融合多源异构信息方面的优势，已成为推荐系统领域的研究热点。与传统的图神经网络（Graph Neural Network, GNN）仅能建模二元关系不同，HGNN通过超边（Hyperedge）结构可以支持任意数量节点之间的高阶关联建模，能够更完整地保留推荐系统中用户—物品交互的多对多、群组化及多模态等复杂语义信息。本文首先从数据输入、超图构建、嵌入学习与推荐输出四个方面给出了HGNN推荐的主要流程；进而从构图策略与特征传播机制两个维度，系统分析了HGNN在序列推荐、多行为推荐、社交推荐、多模态推荐及群组推荐等场景中的研究进展。在HGNN序列推荐研究中，探讨了基于会话交互的局部关联建模、全局共现与跨会话协作建模及多尺度时空动态建模等构图策略，分析了结合超图注意力去噪与自监督对比学习增强的时序特征传播机制。该构图策略与特征传播机制有助于突破图神经网络的“邻居限制”，能更精准地捕捉用户的跳跃性兴趣与全局依赖。在HGNN多行为推荐研究中，分析了特定行为、统一行为及时序行为的超图建模策略，探讨了级联依赖传播、行为感知注意力及跨视图对比学习去噪等特征传播机制，在缓解目标行为稀疏性、促进跨行为语义对齐与知识迁移方面展现出明显优势。在HGNN社交推荐研究中，分析了同质性双视图、异质性语义及隐私保护等构图方法，探讨了融合信任感知注意力与双通道门控融合的传播机制，对拓宽传统二元社交限制、识别复杂群体影响力及高阶社交拓扑结构具有重要意义。在HGNN多模态推荐研究中，探讨了模态分离、协同语义关联及多模态超图优化等构图策略，分析了模态特定卷积聚合与跨模态对比学习对齐的传播机制，其优势在于能显式过滤模态噪声、支持统一语义空间内的高阶推理，从而有助于提升语义理解能力。在HGNN群组推荐研究中，分析了多视图层级对齐、群组结构关联优化及三元关系建模等构图策略，探讨了跨层级反馈与注意力聚合的传播机制，该方法在结构上能更好契合群组的“包含”逻辑，并为缓解偶发群组冷启动问题提供了有效途径。在此基础上，论文指出了现有HGNN推荐研究中存在的动态超图模型难以满足实时推荐的需求、高阶聚合过程中存在不可忽视的信息分辨率损失、超图伪超边结构引入的噪声会干扰模型的鲁棒性、超图计算与存储扩展性问题限制了其在大规模场景中的应用，以及推荐结果的可解释性与公平性不足等问题。针对上述问题，本文从基于生成式自监督解耦的表示学习、轻量化高效训练与推理设计、因果推断驱动的鲁棒性增强、场景感知的多模态融合，以及大语言模型协同增强等多个方面，对HGNN推荐的未来主要研究趋势进行了系统展望，以期为后续研究者提供一些有价值的参考与启发。