Safety-Aware Next-POI Recommendation with Large Language Models
TL;DR
A safety-aware next-POI recommendation method that leverages a Large Language Model (LLM) to generate predictions informed by both mobility patterns and crime-derived safety signals that substantially improves the safety profile of recommended POIs and surpasses state-of-the-art baselines in overall accuracy.
Abstract
Point of Interest (POI) recommendation has become a core task in location-based services, with modern systems increasingly driven by deep learning models that achieve strong predictive accuracy. Yet, despite these advances, most approaches optimize primarily for relevance, giving limited attention to an equally important real-world factor: user safety. In this study, we propose a safety-aware next-POI recommendation method that leverages a Large Language Model (LLM) to generate predictions informed by both mobility patterns and crime-derived safety signals. By integrating crime statistics with POI data and encoding safety information directly into trajectory prompts, our approach produces recommendations that better reflect real-world risk. Through tailored prompt engineering, we finetune an LLM to incorporate safety considerations, yielding predictions that align with user preferences while prioritizing personal security. Experimental results show that our method substantially improves the safety profile of recommended POIs and surpasses state-of-the-art baselines in overall accuracy.