  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.
