用超图+元学习提升冷启动下景点推荐准确率
HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI Recommendation
- 构建三类异构超边捕捉用户行为、空间关系和长期偏好
- 在真实数据集上显著优于基线方法，冷启动场景提升明显
- 适合研究个性化推荐与元学习融合的开发者
下一个兴趣点（POI）推荐旨在通过历史签到序列预测用户的下一个位置。尽管现有方法表现良好，但仍难以捕捉复杂的高阶关系，且在应对用户行为多样性及冷启动问题时效果有限。为此，我们提出超图增强的元学习自适应网络（HyperMAN），将异构超图建模与难度感知的元学习机制结合。具体设计三类异构超边：特定时间下的用户访问行为（时间行为超边）、兴趣点间的空间关联（空间功能超边）以及用户长期偏好（用户偏好超边）。此外，引入多样性感知的元学习机制，动态调整学习策略以适应用户行为差异。在真实数据集上的大量实验表明，HyperMAN性能优越，有效缓解冷启动问题并显著提升推荐精度。
原文摘要 · Abstract (English)
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.
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