Next Point-of-Interest (POI) prediction is a fundamental task in location-based services, especially critical for large-scale navigation platforms like AMAP that serve billions of users across diverse lifestyle scenarios. While recent POI recommendation approaches based on SIDs have achieved promising, they struggle in complex, sparse real-world environments due to two key limitations: (1) inadequate modeling of high-quality SIDs that capture cross-category spatio-temporal collaborative relationships, and (2) poor alignment between large language models (LLMs) and the POI recommendation task. To this end, we propose GeoGR, a geographic generative recommendation framework tailored for navigation-based LBS like AMAP, which perceives users' contextual state changes and enables intent-aware POI recommendation. GeoGR features a two-stage design: (i) a geo-aware SID tokenization pipeline that explicitly learns spatio-temporal collaborative semantic representations via geographically constrained co-visited POI pairs, contrastive learning, and iterative refinement; and (ii) a multi-stage LLM training strategy that aligns non-native SID tokens through multiple template-based continued pre-training(CPT) and enables autoregressive POI generation via supervised fine-tuning(SFT). Extensive experiments on multiple real-world datasets demonstrate GeoGR's superiority over state-of-the-art baselines. Moreover, deployment on the AMAP platform, serving millions of users with multiple online metrics boosting, confirms its practical effectiveness and scalability in production.

翻译：下一个兴趣点（POI）预测是基于位置服务中的一项基础任务，对于像AMAP这样服务数十亿用户、覆盖多样化生活场景的大规模导航平台尤为关键。尽管近期基于语义兴趣标识（SID）的POI推荐方法已取得不错效果，但在复杂、稀疏的真实世界环境中仍面临两个关键局限：（1）对能够捕捉跨类别时空协同关系的高质量SID建模不足；（2）大语言模型（LLM）与POI推荐任务之间的对齐性较差。为此，我们提出GeoGR——一个专为AMAP等导航类LBS定制的地理生成式推荐框架，该框架能够感知用户上下文状态变化，并实现意图感知的POI推荐。GeoGR采用两阶段设计：（i）地理感知的SID标记化流程，通过地理约束的共现POI对、对比学习与迭代优化显式学习时空协同语义表示；（ii）多阶段LLM训练策略，通过基于模板的持续预训练（CPT）对齐非原生SID标记，并借助监督微调（SFT）实现自回归POI生成。在多个真实数据集上的大量实验证明了GeoGR相对于前沿基线方法的优越性。此外，在AMAP平台的实际部署中，通过服务数百万用户并实现多项在线指标提升，验证了其在生产环境中的实用效能与可扩展性。