2025/04/10 by Giovanni Mauro, Marco Minici, Mauro, Giovanni +3 · 1 voice · 2 citations
Computer Science · Engineering · Social Sciences · #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Mobile Crowdsensing and Crowdsourcing #Transportation and Mobility Innovations #cs.AI #cs.CY
paper · pdf · doi:10.48550/arxiv.2504.07911
openalex publication_date 2025/04/10 · arxiv published 2025/04/10 · arxiv updated 2025/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Next-venue recommender systems are increasingly embedded in location-based services, shaping individual mobility decisions in urban environments. While their predictive accuracy has been extensively studied, less attention has been paid to their systemic impact on urban dynamics. In this work, we introduce a simulation framework to model the human-AI feedback loop underpinning next-venue recommendation, capturing how algorithmic suggestions influence individual behavior, which in turn reshapes the data used to retrain the models. Our simulations, grounded in real-world mobility data, systematically explore the effects of algorithmic adoption across a range of recommendation strategies. We find that while recommender systems consistently increase individual-level diversity in visited venues, they may simultaneously amplify collective inequality by concentrating visits on a limited subset of popular places. This divergence extends to the structure of social co-location networks, revealing broader implications for urban accessibility and spatial segregation. Our framework operationalizes the feedback loop in next-venue recommendation and offers a novel lens through which to assess the societal impact of AI-assisted mobility-providing a computational tool to anticipate future risks, evaluate regulatory interventions, and inform the design of ethic algorithmic systems.