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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IntentPOI improves next POI prediction by using a two-stage process of intention inference followed by intention-guided location selection instead of direct trajectory-to-POI mapping.
AgentMob is a training-free LLM-driven agent that formulates mobility prediction as adaptive evidence-controlled decision making and outperforms other training-free LLM methods on three datasets.
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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Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction
IntentPOI improves next POI prediction by using a two-stage process of intention inference followed by intention-guided location selection instead of direct trajectory-to-POI mapping.
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Towards Efficient and Evidence-grounded Mobility Prediction with LLM-Driven Agent
AgentMob is a training-free LLM-driven agent that formulates mobility prediction as adaptive evidence-controlled decision making and outperforms other training-free LLM methods on three 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.