用大模型预测下个位置坐标，支持跨城市泛化。
NextLocLLM: Location Semantics Modeling and Coordinate-Based Next Location Prediction with LLMs
- 将位置预测转为坐标回归，融合大模型理解地点语义。
- 在多城市数据上超越现有方法，零样本迁移效果好。
- 适合需要跨城市预测的移动行为分析场景。
下一个位置预测是人类移动性分析中的关键任务。现有方法通常将其视为基于离散位置ID的分类任务，阻碍了空间连续性建模，并限制了在新城市中的泛化能力。本文提出NextLocLLM，一种新框架，将下一个位置预测重新定义为坐标回归，并集成大语言模型（LLM）进行位置语义编码与坐标级预测。为建模位置功能语义，该方法利用LLM的语言理解能力，从兴趣点（POI）类别的文本描述中提取功能语义，构建增强型POI嵌入。这些嵌入与时空轨迹表示结合后输入同一LLM，实现语义与预测的统一建模。一个轻量级回归头生成坐标输出，再通过后处理检索模块映射到top-k候选位置，确保结构化输出。在多个城市的实验表明，NextLocLLM在监督和零样本设置下均优于现有基线方法。代码已公开：https://github.com/liuwj2000/NexelocLLM。
原文摘要 · Abstract (English)
Next location prediction is a critical task in human mobility analysis.Existing methods typically formulate it as a classification task based on discrete location IDs, which hinders spatial continuity modeling and limits generalization to new cities. In this paper, we propose NextLocLLM, a novel framework that reformulates next-location prediction as coordinate regression and integrates LLMs for both location semantics encoding and coordinate-level prediction. To model location functional semantics, it constructs LLM-enhanced POI embeddings by leveraging language understanding capabilities of LLMs to extract functional semantics from textual descriptions of POI categories. These POI embeddings are combined with spatiotemporal trajectory representation and fed into the same LLM, enabling unified semantic and predictive modeling. A lightweight regression head generates coordinate outputs, which are mapped to top-k candidate locations via post-prediction retrieval module, ensuring structured outputs. Experiments across diverse cities show that NextLocLLM outperforms existing baselines in both supervised and zero-shot settings. Code is available at: https://github.com/liuwj2000/NexelocLLM.
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