Next point-of-interest (POI) recommendation aims to predict the next location a user will visit based on historical behavioral data. Given their strong natural language understanding and reasoning capabilities, large language models (LLMs) have been increasingly introduced into next POI recommendation, as conventional methods are limited by their numerical representations, which often obscure the inherent semantic information embedded in contextual features However, LLM-based approaches struggle to effectively comprehend multiple factors holistically and express them in natural language. To address this issue, we propose Multi-Factor Collaboration for Next POI Recommendation (MFC4POI), a pretraining and fine-tuning framework that reformulates the next POI recommendation task as a question-answering problem. It leverages the strong comprehension and reasoning capabilities of LLMs to analyze user’s current behavior based on historical trajectories, and real time on-site situation, including the dynamic preferences and nearby geographic information in natural language. Due to the inherent limitations of LLMs in accurately modeling temporal and geographical information, MFC4POI introduces an intent identification agent to capture users’ dynamic preferences based on Spatio-temporal Intent-based Knowledge Graph (STIKG). It alleviates the weak spatial reasoning capability of LLMs while providing clear reasoning paths that make the recommendations more intuitive and coherent. To further improve understanding of users’s real-time on-site situation, MFC4POI integrates geographic information with dynamic preferences by providing nearby POIs that align with these dynamic preferences, thereby further enhancing LLMs’ performance. Experiments on three real-world datasets, containing up to 4000 users and 405000 check-ins, show that MFC4POI consistently outperforms strong baselines. Specifically, it achieves better performance than most baselines on Acc@1 and surpasses all baselines on Acc@5 and Acc@10 across all datasets. © 2026 Published by Elsevier Ltd.