Geography-Aware Large Language Models for Next POI Recommendation

• URL: http://arxiv.org/abs/2505.13526v1
• Date: Sun, 18 May 2025 03:20:20 GMT
• Title: Geography-Aware Large Language Models for Next POI Recommendation
• Authors: Zhao Liu, Wei Liu, Huajie Zhu, Jianxing Yu, Jian Yin, Wang-Chien Lee, Shun Wang,
• Abstract summary: Next Point-of-Interest (POI) recommendation task aims to predict users' next destinations based on their historical movement data.<n>We propose GA-LLM (Geography-Aware Large Language Model), a novel framework that enhances Large Language Models with two specialized components.<n>Experiments on three real-world datasets demonstrate the state-of-the-art performance of GA-LLM.
• Score: 21.03555605703108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The next Point-of-Interest (POI) recommendation task aims to predict users' next destinations based on their historical movement data and plays a key role in location-based services and personalized applications. Accurate next POI recommendation depends on effectively modeling geographic information and POI transition relations, which are crucial for capturing spatial dependencies and user movement patterns. While Large Language Models (LLMs) exhibit strong capabilities in semantic understanding and contextual reasoning, applying them to spatial tasks like next POI recommendation remains challenging. First, the infrequent nature of specific GPS coordinates makes it difficult for LLMs to model precise spatial contexts. Second, the lack of knowledge about POI transitions limits their ability to capture potential POI-POI relationships. To address these issues, we propose GA-LLM (Geography-Aware Large Language Model), a novel framework that enhances LLMs with two specialized components. The Geographic Coordinate Injection Module (GCIM) transforms GPS coordinates into spatial representations using hierarchical and Fourier-based positional encoding, enabling the model to understand geographic features from multiple perspectives. The POI Alignment Module (PAM) incorporates POI transition relations into the LLM's semantic space, allowing it to infer global POI relationships and generalize to unseen POIs. Experiments on three real-world datasets demonstrate the state-of-the-art performance of GA-LLM.

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