Mag-Mamba: Modeling Coupled spatiotemporal Asymmetry for POI Recommendation
arXiv:2603.00053v1 Announce Type: new Abstract: Next Point-of-Interest (POI) recommendation is a critical task in location-based services, yet it faces the fundamental challenge of coupled spatiotemporal asymmetry inherent in urban mobility. Specifically, transition intents between locations exhibit high asymmetry and are dynamically conditioned on time. Existing methods, typically built on graph or sequence backbones, rely on symmetric operators or real-valued aggregations, struggling to unify the modeling of time-varying global directionality. To address this limitation, we propose Mag-Mamba, a framework whose core insight lies in modeling spatiotemporal asymmetry as phase-driven rotational dynamics in the complex domain. Based on this, we first devise a Time-conditioned Magnetic Phase Encoder that constructs a time-conditioned Magnetic Laplacian on the geographic adjacency graph, utilizing edge phase differences to characterize the globally evolving spatial directionality. Subseque
arXiv:2603.00053v1 Announce Type: new Abstract: Next Point-of-Interest (POI) recommendation is a critical task in location-based services, yet it faces the fundamental challenge of coupled spatiotemporal asymmetry inherent in urban mobility. Specifically, transition intents between locations exhibit high asymmetry and are dynamically conditioned on time. Existing methods, typically built on graph or sequence backbones, rely on symmetric operators or real-valued aggregations, struggling to unify the modeling of time-varying global directionality. To address this limitation, we propose Mag-Mamba, a framework whose core insight lies in modeling spatiotemporal asymmetry as phase-driven rotational dynamics in the complex domain. Based on this, we first devise a Time-conditioned Magnetic Phase Encoder that constructs a time-conditioned Magnetic Laplacian on the geographic adjacency graph, utilizing edge phase differences to characterize the globally evolving spatial directionality. Subsequently, we introduce a Complex-valued Mamba module that generalizes traditional scalar state decay into joint decay-rotation dynamics, explicitly modulated by both time intervals and magnetic geographic priors. Extensive experiments on three real-world datasets demonstrate that Mag-Mamba achieves significant performance improvements over state-of-the-art baselines.
Executive Summary
The article proposes Mag-Mamba, a novel framework for next point-of-interest recommendation in location-based services. Mag-Mamba addresses the challenge of coupled spatiotemporal asymmetry by modeling it as phase-driven rotational dynamics in the complex domain. The framework consists of a Time-conditioned Magnetic Phase Encoder and a Complex-valued Mamba module. Experimental results demonstrate significant performance improvements over state-of-the-art baselines on three real-world datasets. The proposed framework is particularly useful for modeling time-varying global directionality and asymmetric transition intents between locations. However, further research is needed to explore the potential applications and limitations of Mag-Mamba in real-world scenarios.
Key Points
- ▸ Mag-Mamba addresses the challenge of coupled spatiotemporal asymmetry in location-based services.
- ▸ The framework models spatiotemporal asymmetry as phase-driven rotational dynamics in the complex domain.
- ▸ Experimental results demonstrate significant performance improvements over state-of-the-art baselines.
Merits
Strength in Modeling Asymmetry
Mag-Mamba's ability to model coupled spatiotemporal asymmetry as phase-driven rotational dynamics in the complex domain is a significant strength, allowing for more accurate modeling of time-varying global directionality and asymmetric transition intents between locations.
Demerits
Limited Exploration of Applications
The article primarily focuses on the theoretical framework and experimental results, with limited exploration of potential applications and limitations of Mag-Mamba in real-world scenarios.
Expert Commentary
Mag-Mamba is a significant contribution to the field of location-based services, addressing a long-standing challenge in next point-of-interest recommendation. The framework's ability to model coupled spatiotemporal asymmetry as phase-driven rotational dynamics in the complex domain is particularly noteworthy. However, further research is needed to explore the potential applications and limitations of Mag-Mamba in real-world scenarios. Additionally, the article's findings have implications for urban planning and mobility studies, highlighting the importance of considering coupled spatiotemporal asymmetry in modeling urban mobility patterns.
Recommendations
- ✓ Future researchers should explore the potential applications of Mag-Mamba in real-world scenarios, such as urban mobility planning and location-based recommendation systems.
- ✓ The authors should provide a more detailed exploration of the limitations and potential biases of Mag-Mamba, including the assumptions and constraints underlying the framework.