HyperMAN：基于超图增强的元学习自适应网络用于下一个兴趣点推荐
HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI Recommendation
AI总结：
针对下一个兴趣点推荐中的高阶关系建模和冷启动问题，提出超图增强元学习自适应网络，通过异构超边和多样性感知元学习提升推荐准确性。
摘要：
下一个兴趣点（POI）推荐旨在通过利用历史签到序列来预测用户的下一位置。尽管现有方法已显示出有前景的结果，但它们通常难以捕获复杂的高阶关系，并有效适应多样化的用户行为，特别是在处理冷启动问题时。为解决这些挑战，我们提出了超图增强的元学习自适应网络（HyperMAN），一种将异构超图建模与难度感知的元学习机制相结合的新框架，用于下一个POI推荐。具体而言，设计了三种类型的异构超边来捕获高阶关系：特定时间的用户访问行为（时间行为超边）、POI之间的空间相关性（空间功能超边）以及用户长期偏好（用户偏好超边）。此外，引入了一种多样性感知的元学习机制，以动态调整学习策略，考虑用户行为的多样性。在真实世界数据集上的大量实验表明，HyperMAN实现了优越的性能，有效解决了冷启动挑战，并显著提高了推荐准确性。
英文摘要：
Next Point-of-Interest (POI) recommendation aims to predict users' next locations by leveraging historical check-in sequences. Although existing methods have shown promising results, they often struggle to capture complex high-order relationships and effectively adapt to diverse user behaviors, particularly when addressing the cold-start issue. To address these challenges, we propose Hypergraph-enhanced Meta-learning Adaptive Network (HyperMAN), a novel framework that integrates heterogeneous hypergraph modeling with a difficulty-aware meta-learning mechanism for next POI recommendation. Specifically, three types of heterogeneous hyperedges are designed to capture high-order relationships: user visit behaviors at specific times (Temporal behavioral hyperedge), spatial correlations among POIs (spatial functional hyperedge), and user long-term preferences (user preference hyperedge). Furthermore, a diversity-aware meta-learning mechanism is introduced to dynamically adjust learning strategies, considering users behavioral diversity. Extensive experiments on real-world datasets demonstrate that HyperMAN achieves superior performance, effectively addressing cold start challenges and significantly enhancing recommendation accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
