Work

TourismMinds: A Geo-augmented LLM Framework for Semantic-aware Trajectory Analytics and Generation

Year: 2025

Type: article

Abstract: With the rise of smart cities and digital tourism, understanding tourist movement is essential for enhancing personalized experiences and informing business decisions. Traditional methods struggle to integrate multi-source data and interpret complex behaviors, while large language models (LLMs) offer untapped potential. This study proposes an LLM-driven “Perception-Modeling-Generation” framework, validated by a Kulangsu smart tourism case. First, a five-stage spatiotemporal-semantic alignment pipeline integrates GPS, social media, POI, and weather data. Second, to address LLM limitations, we introduce a knowledge-enhanced framework combining geospatially-aware LoRA fine-tuning and a hierarchical Retrieval-Augmented Generation (RAG) mechanism. This approach reduces hallucinations by 15% and boosts geographic reasoning accuracy, achieving state-of-the-art intent recognition (15% gain) and landmark identification (72% accuracy). Third, we design a reprogramming strategy with geo-semantic constraints for generating spatially plausible, personalized trajectories in data-scarce settings, showing 89% similarity to real-world data. Supported by the Kulangsu Smart Tourism System, our method provides a scalable paradigm for spatiotemporal modeling and synthetic trajectory generation, benefiting personalized services and decision-making.

Source: Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

DOI: 10.1145/3770697