Lumi: An LLM-Grounded System for Safety-Aware POI Recommendations

Abstract

Point-of-interest (POI) recommender systems typically optimize for accurate next-POI prediction, while personal safety considerations remain implicit or absent. In this demonstration, we present Lumi, a safety-aware POI recommendation system that integrates city-specific contextual features into an LLM-based recommendation process. Lumi combines crime-related context, street lighting, temporal attributes, weather conditions, holiday indicators, POI category information, and user-defined visit preferences. A key feature of Lumi is a user-controlled cautiousness mechanism, allowing users to explicitly adjust the desired level of safety sensitivity for each search. The system is implemented as a mobile application connected to a WebSocket-based backend that enriches user requests with structured urban context and returns POI recommendations with human-readable safety and relevance explanations. Lumi demonstrates how LLMs can be grounded in city-scale contextual data to support explainable, safety-aware recommendations.