T-LENs: A Tile-Assisted Prompt Framework for Next Location Prediction via Large Language Models

Next location prediction is critical for personalized recommendations, transportation planning, and emergency responses. However, the sparsity of mobility data and the stochastic nature of individuals daily activities make accurate forecasting still a significant challenge. Existing next location prediction methods often rely on discrete location IDs from limited-scale datasets, limiting interpretability and generalization across regions. To address these issues, we propose T-LENs, a prompt-based framework that combines continuous tile-assisted spatial encoding with the interpretive and reasoning capabilities of Large Language Models LLMs . Our proposed tile-assisted encoding integrates seamlessly with existing methods and enhances privacy preservation by avoiding exposure of sensitive raw coordinates, while also mitigating noise from ultra-precise geolocation data. Furthermore, T-LENs models human mobility by jointly capturing long-term trends and short-term dependencies through a variable-length window, enabling LLMs to identify complex mobility patterns with high accuracy. Our experiments demonstrate that T-LENs significantly outperforms state-of-the-art baselines, achieving superior prediction accuracy with a 50 improvement in Acc@1 and 8 in nDCG@10, while requiring no dataset-specific training. To comprehensively assess the frameworks adaptability, we further evaluate its performance across diverse LLMs, highlighting their potential and limitations in mobility modeling.