Next Point-of-interest (POI) recommendation provides valuable suggestions for users to explore their surrounding environment. Existing studies rely on building recommendation models from large-scale users' check-in data, which is task-specific and needs extensive computational resources. Recently, the pretrained large language models (LLMs) have achieved significant advancements in various NLP tasks and have also been investigated for recommendation scenarios. However, the generalization abilities of LLMs still are unexplored to address the next POI recommendations, where users' geographical movement patterns should be extracted. Although there are studies that leverage LLMs for next-item recommendations, they fail to consider the geographical influence and sequential transitions. Hence, they cannot effectively solve the next POI recommendation task. To this end, we design novel prompting strategies and conduct empirical studies to assess the capability of LLMs, e.g., ChatGPT, for predicting a user's next check-in. Specifically, we consider several essential factors in human movement behaviors, including user geographical preference, spatial distance, and sequential transitions, and formulate the recommendation task as a ranking problem. Through extensive experiments on two widely used real-world datasets, we derive several key findings. Empirical evaluations demonstrate that LLMs have promising zero-shot recommendation abilities and can provide accurate and reasonable predictions. We also reveal that LLMs cannot accurately comprehend geographical context information and are sensitive to the order of presentation of candidate POIs, which shows the limitations of LLMs and necessitates further research on robust human mobility reasoning mechanisms.

翻译：下一次兴趣点（POI）推荐为用户探索周边环境提供了有价值的建议。现有研究依赖于从大规模用户签到数据中构建推荐模型，这种方法具有任务特异性，且需要大量计算资源。近年来，预训练大语言模型（LLMs）在各种自然语言处理任务中取得了显著进展，并已被探索用于推荐场景。然而，LLMs在解决下一次POI推荐任务时的泛化能力仍未得到充分研究，因为该任务需要提取用户的地理移动模式。尽管已有研究利用LLMs进行下一次物品推荐，但它们未能考虑地理影响和序列转移，因此无法有效解决下一次POI推荐任务。为此，我们设计了新颖的提示策略，并开展实证研究评估LLMs（如ChatGPT）预测用户下一次签到行为的能力。具体来说，我们考虑了人类移动行为中的几个关键因素，包括用户地理偏好、空间距离和序列转移，并将推荐任务表述为排序问题。通过在两个广泛使用的真实世界数据集上进行大量实验，我们得出了若干关键发现。实证评估表明，LLMs具有有前景的零样本推荐能力，能够提供准确且合理的预测。我们还发现，LLMs无法准确理解地理上下文信息，并且对候选POI的呈现顺序敏感，这揭示了LLMs的局限性，并需要进一步研究稳健的人类移动性推理机制。