Next Point-of-Interest (POI) recommendation is a fundamental task in location-based services. While recent advances leverage Large Language Model (LLM) for sequential modeling, existing LLM-based approaches face two key limitations: (i) strong reliance on the contextual completeness of user histories, resulting in poor performance on out-of-history (OOH) scenarios; (ii) limited scalability, due to the restricted context window of LLMs, which limits their ability to access and process a large number of candidate POIs. To address these challenges, we propose Tool4POI, a novel tool-augmented framework that enables LLMs to perform open-set POI recommendation through external retrieval and reasoning. Tool4POI consists of three key modules: preference extraction module, multi-turn candidate retrieval module, and reranking module, which together summarize long-term user interests, interact with external tools to retrieve relevant POIs, and refine final recommendations based on recent behaviors. Unlike existing methods, Tool4POI requires no task-specific fine-tuning and is compatible with off-the-shelf LLMs in a plug-and-play manner. Extensive experiments on three real-world datasets show that Tool4POI substantially outperforms state-of-the-art baselines, achieving up to 40% accuracy on challenging OOH scenarios where existing methods fail, and delivering average improvements of 20% and 30% on Acc@5 and Acc@10, respectively.

翻译：下一个兴趣点推荐是基于位置服务中的一项基础任务。尽管近期研究利用大语言模型进行序列建模，但现有基于大语言模型的方法面临两个关键局限：（i）对用户历史记录上下文完整性的强依赖，导致在历史外场景中性能不佳；（ii）可扩展性受限，由于大语言模型的上下文窗口有限，限制了其访问和处理大量候选兴趣点的能力。为应对这些挑战，我们提出了TOOL4POI，一种新颖的工具增强型框架，使大语言模型能够通过外部检索和推理执行开放集兴趣点推荐。TOOL4POI包含三个关键模块：偏好提取模块、多轮候选检索模块和重排序模块，它们共同总结用户的长期兴趣，与外部工具交互以检索相关兴趣点，并基于近期行为优化最终推荐。与现有方法不同，TOOL4POI无需任务特定微调，并能以即插即用方式兼容现成大语言模型。在三个真实世界数据集上的大量实验表明，TOOL4POI显著优于最先进的基线方法，在现有方法失效的挑战性历史外场景中实现了高达40%的准确率，并在Acc@5和Acc@10指标上分别平均提升了20%和30%。