H-RLPOI: A Hybrid LLM and Reinforcement Learning Framework for Next POI Recommendation

Abstract

Large Language Models (LLMs) have recently been explored for next Point-of-Interest (POI) recommendation. Despite progress, existing approaches face three fundamental challenges: (i) POIs are often represented by simple identifiers or categorical labels, overlooking rich textual semantics; (ii) The task of predicting the next POI is inherently sequential and context-dependent, requiring models to reason over user histories, temporal dynamics, and environmental factors; (iii) Supervised fine-tuning provides only a single predicted POI, ignoring the capacity of LLMs to generate $k$ POIs candidates. To address these issues, we propose H-RLPOI, Hybrid LLM and Reinforcement Learning Framework for next POI Recommendation, that enhances LLM representations by injecting semantic POI embeddings through token-level alignment and applies reinforcement learning with Proximal Policy Optimization (PPO) as a decision layer to optimize POI selection conditioned on user trajectories. Experiments on real-world datasets show that H-RLPOI provides context-aware, semantically grounded, and adaptive recommendations, achieving competitive or stateof-the-art performance depending on the dataset.