Point-of-Interest (POI) retrieval aims to identify relevant candidates from massive-scale POI databases, serving as a cornerstone for diverse location-based services. However, in general map search scenarios, conventional POI retrieval methods are increasingly challenged by underspecified user queries due to their excessive reliance on surface-level semantic matching. Meanwhile, such queries are often highly context-dependent and personalized, yet existing retrieval paradigms struggle to effectively synergize heterogeneous contexts for complex search intent inference. To address these limitations, we revisit general map search from a generative perspective and propose GenPOI, an innovative Generative POI retrieval framework tailored for general search on maps. It seamlessly unifies heterogeneous search contexts and POIs into structured sequences, leveraging the powerful contextual modeling of Large Language Models (LLMs) for spatial-aware candidate generation. Consequently, this generative paradigm effectively solves more challenging queries through profound context dependency modeling and search intent reasoning. Specifically, accounting for the unique geospatial nature of map scenarios, GenPOI introduces a novel Geo-Semantic POI Tokenization to represent each POI as a compact token sequence encoding both semantic and geographic context, thus grounding the LLM's spatial understanding. Additionally, a proximity-aware constrained generation strategy is employed to restrict the decoding space of the LLM, ensuring the validity and geospatial relevance of the generated results. Extensive experiments on large-scale industrial datasets from Tencent Map, comprising POIs at the scale of over 10 million, demonstrate the superior performance of GenPOI.

翻译：兴趣点（POI）检索旨在从海量POI数据库中识别相关候选对象，是各类基于位置服务的基石。然而，在通用地图搜索场景中，传统POI检索方法因过度依赖表层语义匹配，逐渐难以应对用户查询表述不明确的问题。同时，此类查询通常高度依赖上下文且具有个性化特征，但现有检索范式难以有效融合异构上下文以推断复杂搜索意图。针对上述局限，我们从生成式视角重新审视通用地图搜索，并提出GenPOI——一种专为地图通用搜索设计的创新生成式POI检索框架。该框架将异构搜索上下文与POI无缝统一为结构化序列，借助大语言模型（LLMs）强大的上下文建模能力实现空间感知的候选生成。通过深度上下文依赖性建模与搜索意图推理，该生成式范式有效解决了更具挑战性的查询问题。具体而言，针对地图场景独特的地理空间特性，GenPOI引入了一种新颖的地理语义POI分词方法，将每个POI表示为同时编码语义与地理上下文的紧凑词元序列，从而奠定LLM的空间理解基础。此外，还采用邻近感知约束生成策略来限制LLM的解码空间，确保生成结果的有效性与地理空间相关性。基于腾讯地图超千万级POI的大规模工业数据集实验表明，GenPOI具有卓越性能。