Playing It Safe: Risk-Aware Recommendation Systems for Smart Spaces

Recommendation systems have demonstrated success in suggesting relevant items to users by efficiently filtering through a wide range of potential options. However, recommender systems used in physical environments need to find a balance between the relevance of suggestions and potential contextual risks. This paper introduces a novel type of risk-aware recommender system (RARS) designed for smart spaces, where the user movements and the room occupancy influence both the quality and risk of recommendations. Using the Museum of Modern Art (MoMA) as a case study, we integrate a virus contagion model into the recommendation process to minimize exposure risk. A knowledge graph-based architecture is employed to generate content-based recommendations, enriched through semantic embeddings. We propose and evaluate multiple reward functions that integrate both recommendation scores and infection risk using different normalization score strategies. An exhaustive set of simulations prove how various decision-making strategies and risk thresholds affect the recommendation quality, infection risk, and movement efficiency. Our findings reveal that RARS can significantly reduce user exposure while preserving a satisfactory relevance of recommendations, offering a generalizable framework for safety-aware recommender systems in public venues.