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：下一兴趣点（POI）推荐为用户探索周边环境提供有价值的建议。现有研究依赖于从大规模用户签到数据构建推荐模型，这类方法具有任务特异性且需要大量计算资源。近期，预训练大型语言模型（LLM）在各类自然语言处理任务中取得显著进展，并开始被探索应用于推荐场景。然而，LLM在解决需提取用户地理移动模式的下一POI推荐问题时的泛化能力尚未得到充分研究。尽管已有研究利用LLM进行下一项目推荐，但未能考虑地理影响与序列转换，因此无法有效解决下一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LLM（如ChatGPT）预测用户下一次签到的能力。具体而言，我们考虑了人类移动行为中的若干关键因素（包括用户地理偏好、空间距离和序列转换），并将推荐任务形式化为排序问题。通过在两个广泛使用的真实世界数据集上进行大量实验，我们得出若干关键发现。实证评估表明，LLM具有优异的零样本推荐能力，能够提供准确合理的预测。同时研究揭示，LLM无法准确理解地理上下文信息，且对候选POI的呈现顺序敏感——这揭示了LLM的局限性，并表明需要进一步研究稳健的人类移动推理机制。