Multifaceted Scenario-Aware Hypergraph Learning for Next POI Recommendation

• URL: http://arxiv.org/abs/2601.11610v1
• Date: Fri, 09 Jan 2026 06:29:55 GMT
• Title: Multifaceted Scenario-Aware Hypergraph Learning for Next POI Recommendation
• Authors: Yuxi Lin, Yongkang Li, Jie Xing, Zipei Fan,
• Abstract summary: Next Point-of-Interest (POI) recommendation plays a crucial role in inferring user preferences from historical check-in trajectories.<n>Existing sequential and graph-based methods frequently neglect significant mobility variations across distinct contextual scenarios.<n>We propose the Multifaceted Scenario-Aware Hypergraph Learning method (MSAHG), a framework that adopts a scenario-splitting paradigm for next POI recommendation.
• Score: 6.180520055741916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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