mlsanityData to 14 Oct 2026

The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation

arXiv 2504.07911 ↗First posted 10 Apr 2025cs.AI cs.CYcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 75 references that are in the map. First posted on arXiv 10 Apr 2025.

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References · 24

  1. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  2. 2018Variational Autoencoders for Collaborative Filteringcited by 310
  3. 2023Generative artificial intelligence enhances creativity but reduces the diversity of novel contentcited by 167
  4. 2017How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utilitycited by 120
  5. 2017Runaway Feedback Loops in Predictive Policingcited by 97
  6. 2020Feedback Loop and Bias Amplification in Recommender Systemscited by 90
  7. 2020A Survey on Deep Learning for Human Mobilitycited by 88
  8. 2019Degenerate Feedback Loops in Recommender Systemscited by 67
  9. 2023A Survey on Popularity Bias in Recommender Systemscited by 43
  10. 2021The Effect of People Recommenders on Echo Chambers and Polarizationcited by 20
  11. 2016Data-driven generation of spatio-temporal routines in human mobilitycited by 19
  12. 2024Future Directions in Human Mobility Sciencecited by 16
12 more
  1. 2020Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendationcited by 12
  2. 2021Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?cited by 9
  3. 2019Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparisoncited by 6
  4. 2024Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferencescited by 5
  5. 2022How Routing Strategies Impact Urban Emissionscited by 4
  6. 2024Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networkscited by 4
  7. 2015Coupling Human Mobility and Social Tiescited by 3
  8. 2021Quantifying the sustainability impact of Google Maps: A case study of Salt Lake Citycited by 3
  9. 2022Trajectory Test-Train Overlap in Next-Location Prediction Datasetscited by 3
  10. 2024A survey on the impacts of recommender systems on users, items, and human-AI ecosystemscited by 3
  11. 2023One-Shot Traffic Assignment with Forward-Looking Penalizationcited by 2
  12. 2024Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systemscited by 1

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