RECAP improves next-POI prediction by reconstructing sparse transitions via multi-hop graph transitivity and user revisit signals, yielding gains on tail transitions across real datasets.
Where Would I Go Next? Large Language Models as Human Mobility Predictors
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ARMove is a transferable framework for human mobility prediction that combines agentic LLM reasoning, feature management, and large-small model synergy to outperform baselines on several metrics while improving interpretability and robustness.
Heuristic demonstration selection methods outperform embedding-based methods for practical LLM-based next POI prediction on three real-world datasets.
citing papers explorer
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Beyond Long Tail POIs: Transition-Centered Generalization for Human Mobility Prediction
RECAP improves next-POI prediction by reconstructing sparse transitions via multi-hop graph transitivity and user revisit signals, yielding gains on tail transitions across real datasets.
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ARMove: Learning to Predict Human Mobility through Agentic Reasoning
ARMove is a transferable framework for human mobility prediction that combines agentic LLM reasoning, feature management, and large-small model synergy to outperform baselines on several metrics while improving interpretability and robustness.
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A Comparative Study of Demonstration Selection for Practical Large Language Models-based Next POI Prediction
Heuristic demonstration selection methods outperform embedding-based methods for practical LLM-based next POI prediction on three real-world datasets.