NextLocLLM: Location Semantics Modeling and Coordinate-Based Next Location Prediction with LLMs
Shuai Liu, Ning Cao, Yile Chen, Yue Jiang, George Rosario Jagadeesh, Gao Cong

TL;DR
NextLocLLM introduces a coordinate regression approach using LLMs for human mobility prediction, capturing location semantics and spatial continuity, and outperforms existing methods in diverse city datasets.
Contribution
It reformulates next-location prediction as coordinate regression with LLM-enhanced semantic modeling, enabling better generalization and spatial continuity handling.
Findings
Outperforms baselines in diverse city datasets
Effective in supervised and zero-shot settings
Leverages LLMs for semantic and coordinate prediction
Abstract
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,…
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Taxonomy
TopicsGeographic Information Systems Studies · Data Management and Algorithms
