用检索增强让大模型零样本推荐下一个景点，还加了地理排序
RALLM-POI: Retrieval-Augmented LLM for Zero-shot Next POI Recommendation with Geographical Reranking
- 用历史轨迹检索器找相关移动记录作上下文参考
- 地理距离重排器优先筛选空间上相近的轨迹
- 大模型自我修正输出，适合无训练场景的推荐应用
下一兴趣点（POI）推荐旨在根据用户历史移动行为预测其下一个目的地。传统模型需大量训练，而大语言模型（LLM）虽具零样本泛化能力，却常因缺乏轨迹与空间上下文生成通用或地理无关结果。为此，我们提出RALLM-POI框架，结合检索增强生成与自校正机制。首先设计历史轨迹检索器（HTR），检索相关历史轨迹作为上下文参考；再通过地理距离重排器（GDR）对检索结果进行空间相关性排序；最后由代理式大模型校正器（ALR）通过自我反思优化输出。该方法无需额外训练，在三个真实Foursquare数据集上显著提升准确率，超越传统及基于LLM的基线方法。代码已开源：https://github.com/LKRcrocodile/RALLM-POI。
原文摘要 · Abstract (English)
Next point-of-interest (POI) recommendation predicts a user's next destination from historical movements. Traditional models require intensive training, while LLMs offer flexible and generalizable zero-shot solutions but often generate generic or geographically irrelevant results due to missing trajectory and spatial context. To address these issues, we propose RALLM-POI, a framework that couples LLMs with retrieval-augmented generation and self-rectification. We first propose a Historical Trajectory Retriever (HTR) that retrieves relevant past trajectories to serve as contextual references, which are then reranked by a Geographical Distance Reranker (GDR) for prioritizing spatially relevant trajectories. Lastly, an Agentic LLM Rectifier (ALR) is designed to refine outputs through self-reflection. Without additional training, RALLM-POI achieves substantial accuracy gains across three real-world Foursquare datasets, outperforming both conventional and LLM-based baselines. Code is released at https://github.com/LKRcrocodile/RALLM-POI.
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