Predicting the locations an individual will visit in the future is crucial
for solving many societal issues like disease diffusion and reduction of
pollution among many others. The models designed to tackle next-location
prediction, however, require a significant amount of individual-level
information to be trained effectively. Such data may be scarce or eve
通过对两个广泛使用的真实世界数据集进行广泛实验，我们得出了几个重要的发现。实证评估表明，大型语言模型具有有希望的零样本推荐能力，可以提供准确合理的预测。我们还揭示了大型语言模型不能准确理解地理上下文信息，并对候选 POI 的展示顺序敏感，这显示了大型语言模型的局限性并需要进一步研究鲁棒的人类移动推理机制。