Refine-POI: Reinforcement Fine-Tuned Large Language Models for Next Point-of-Interest Recommendation

Refine-POI introduces a reinforcement fine-tuned framework for next point-of-interest recommendation that tackles two key issues: topology-blind indexing of semantic IDs and the limitation of supervised fine-tuning to top‑1 predictions. It uses a hierarchical self‑organizing map to generate topology‑aware semantic IDs and a policy‑gradient approach to produce top‑k recommendation lists. Experiments on three real‑world datasets show that Refine‑POI outperforms state‑of‑the‑art baselines, combining LLM reasoning with accurate, explainable recommendations.

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• llms
• diffusion
• fine-tuning
• efficiency
• benchmarks