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

• URL: http://arxiv.org/abs/2506.21599v2
• Date: Mon, 30 Jun 2025 11:36:12 GMT
• Title: Refine-POI: Reinforcement Fine-Tuned Large Language Models for Next Point-of-Interest Recommendation
• Authors: Peibo Li, Shuang Ao, Hao Xue, Yang Song, Maarten de Rijke, Johan Barthélemy, Tomasz Bednarz, Flora D. Salim,
• Abstract summary: Large language models (LLMs) have been adopted for next point-of-interest (POI) recommendation tasks.<n>We propose Refine-POI, a reinforcement fine-tuning framework for next POI recommendation.
• Score: 51.08869388483333
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large language models (LLMs) have been adopted for next point-of-interest (POI) recommendation tasks. Typical LLM-based recommenders fall into two categories: prompt-based and supervised fine-tuning (SFT)-based models. Prompt-based models generally offer greater output flexibility but deliver lower accuracy, whereas SFT-based models achieve higher performance yet face a fundamental mismatch: next POI recommendation data does not naturally suit supervised fine-tuning. In SFT, the model is trained to reproduce the exact ground truth, but each training example provides only a single target POI, so there is no ground truth for producing a top-k list. To address this, we propose Refine-POI, a reinforcement fine-tuning framework for next POI recommendation. We introduce recommendation-driven rewards that enable LLMs to learn to generate top-k recommendation lists using only one ground-truth POI per example. Experiments on real-world datasets demonstrate that Refine-POI achieves state-of-the-art top-k recommendation performance.

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