Point-of-Interest Recommendations and Large Language Models: A Powerful Combination

Point-of-interest (POI) recommendation systems have become a cornerstone of personalized user experiences in various applications, such as navigation, tourism, and retail. Recent advancements in artificial intelligence, particularly the integration of large language models (LLMs), have opened new possibilities for building a more powerful and comprehensive POI recommendation system. This work first reviews the latest research outcomes that leverage LLMs for POI recommendation, highlighting their contribution to dealing with the long-standing challenges of POI recommendation systems. Furthermore, we identify untapped potentials of LLM-based POI recommenders across the recommendation pipeline, encompassing multimodal feature augmentation, unified encoding, hybrid task learning for scoring and ranking, and dynamic user interaction. By exploring these opportunities, future research can transform POI recommendation systems into adaptive, user-centric assistants, delivering seamless, scalable, and personalized experiences. This article aims to provide a comprehensive overview of recent advancements and propose future directions for leveraging LLMs to redefine the next generation of POI recommendation systems.