Why Users Go There: World Knowledge-Augmented Generative Next POI Recommendation

arXiv:2605.11807v1 Announce Type: new Abstract: Generative point-of-interest (POI) recommendation models based on large language models (LLMs) have shown promising results by formulating next POI prediction as a sequence generation task. However, the knowledge encoded in these models remains fixed after training, making them unable to perceive evolving real-world conditions that shape user mobility decisions, such as local events and cultural trends. To bridge this gap, we propose AWARE (Agent-b