区分用户场景，用超图学习预测下一个打卡点。
Multifaceted Scenario-Aware Hypergraph Learning for Next POI Recommendation
- 拆分不同用户场景构建多视角超图，捕捉移动规律。
- 参数分流机制缓解跨场景冲突，提升推荐准确率。
- 适合研究位置推荐与用户行为建模的开发者。
在基于位置的社会网络中，下一兴趣点（POI）推荐对从历史签到轨迹中推断用户偏好至关重要。然而，现有序列与图模型常忽略不同情境下（如游客与本地人）显著的移动差异，导致性能不佳，根源在于无法捕捉情境特异性特征，且难以解决跨情境固有冲突。为此，本文提出多面情境感知超图学习框架（MSAHG），采用情境分割范式进行下一POI推荐。主要贡献包括：(1) 构建情境特异、多视角解耦的子超图以捕捉不同移动模式；(2) 设计参数分流机制，自适应化解不同情境间的优化冲突，同时保持泛化能力。在三个真实数据集上的大量实验表明，MSAHG在多种情境下持续优于五种先进方法，验证了其在多情境POI推荐中的有效性。
原文摘要 · Abstract (English)
Among the diverse services provided by Location-Based Social Networks (LBSNs), Next Point-of-Interest (POI) recommendation plays a crucial role in inferring user preferences from historical check-in trajectories. However, existing sequential and graph-based methods frequently neglect significant mobility variations across distinct contextual scenarios (e.g., tourists versus locals). This oversight results in suboptimal performance due to two fundamental limitations: the inability to capture scenario-specific features and the failure to resolve inherent inter-scenario conflicts. To overcome these limitations, we propose the Multifaceted Scenario-Aware Hypergraph Learning method (MSAHG), a framework that adopts a scenario-splitting paradigm for next POI recommendation. Our main contributions are: (1) Construction of scenario-specific, multi-view disentangled sub-hypergraphs to capture distinct mobility patterns; (2) A parameter-splitting mechanism to adaptively resolve conflicting optimization directions across scenarios while preserving generalization capability. Extensive experiments on three real-world datasets demonstrate that MSAHG consistently outperforms five state-of-the-art methods across diverse scenarios, confirming its effectiveness in multi-scenario POI recommendation.
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