RALLM-POI: Retrieval-Augmented LLM for Zero-shot Next POI Recommendation with Geographical Reranking

• URL: http://arxiv.org/abs/2509.17066v1
• Date: Sun, 21 Sep 2025 12:52:28 GMT
• Title: RALLM-POI: Retrieval-Augmented LLM for Zero-shot Next POI Recommendation with Geographical Reranking
• Authors: Kunrong Li, Kwan Hui Lim,
• Abstract summary: Next point-of-interest (POI) recommendation predicts a user's next destination from historical movements.<n>Traditional models require intensive training, while LLMs offer flexible and generalizable zero-shot solutions.<n>We propose RALLM-POI, a framework that couples LLMs with retrieval-augmented generation and self-rectification.
• Score: 7.085868567930685
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Next point-of-interest (POI) recommendation predicts a user's next destination from historical movements. Traditional models require intensive training, while LLMs offer flexible and generalizable zero-shot solutions but often generate generic or geographically irrelevant results due to missing trajectory and spatial context. To address these issues, we propose RALLM-POI, a framework that couples LLMs with retrieval-augmented generation and self-rectification. We first propose a Historical Trajectory Retriever (HTR) that retrieves relevant past trajectories to serve as contextual references, which are then reranked by a Geographical Distance Reranker (GDR) for prioritizing spatially relevant trajectories. Lastly, an Agentic LLM Rectifier (ALR) is designed to refine outputs through self-reflection. Without additional training, RALLM-POI achieves substantial accuracy gains across three real-world Foursquare datasets, outperforming both conventional and LLM-based baselines. Code is released at https://github.com/LKRcrocodile/RALLM-POI.

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