GTR-Mamba: Geometry-to-Tangent Routing for Hyperbolic POI Recommendation

• URL: http://arxiv.org/abs/2510.22942v1
• Date: Mon, 27 Oct 2025 02:56:08 GMT
• Title: GTR-Mamba: Geometry-to-Tangent Routing for Hyperbolic POI Recommendation
• Authors: Zhuoxuan Li, Jieyuan Pei, Tangwei Ye, Zhongyuan Lai, Zihan Liu, Fengyuan Xu, Qi Zhang, Liang Hu,
• Abstract summary: GTR-Mamba is a novel framework for cross-manifold conditioning and routing.<n>It consistently outperforms state-of-the-art baseline models in next POI recommendation.
• Score: 19.900436178093027
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Next Point-of-Interest (POI) recommendation is a critical task in modern Location-Based Social Networks (LBSNs), aiming to model the complex decision-making process of human mobility to provide personalized recommendations for a user's next check-in location. Existing POI recommendation models, predominantly based on Graph Neural Networks and sequential models, have been extensively studied. However, these models face a fundamental limitation: they struggle to simultaneously capture the inherent hierarchical structure of spatial choices and the dynamics and irregular shifts of user-specific temporal contexts. To overcome this limitation, we propose GTR-Mamba, a novel framework for cross-manifold conditioning and routing. GTR-Mamba leverages the distinct advantages of different mathematical spaces for different tasks: it models the static, tree-like preference hierarchies in hyperbolic geometry, while routing the dynamic sequence updates to a novel Mamba layer in the computationally stable and efficient Euclidean tangent space. This process is coordinated by a cross-manifold channel that fuses spatio-temporal information to explicitly steer the State Space Model (SSM), enabling flexible adaptation to contextual changes. Extensive experiments on three real-world datasets demonstrate that GTR-Mamba consistently outperforms state-of-the-art baseline models in next POI recommendation.

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