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

arXiv: https://arxiv.org/abs/2504.07911 · HTML: https://mlsanity.com/paper/2504.07911/

## 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.

## Builds on (most cited first, 24 of 24)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility](https://mlsanity.com/paper/1710.11214/) (2017)
- [Runaway Feedback Loops in Predictive Policing](https://mlsanity.com/paper/1706.09847/) (2017)
- [Feedback Loop and Bias Amplification in Recommender Systems](https://mlsanity.com/paper/2007.13019/) (2020)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Degenerate Feedback Loops in Recommender Systems](https://mlsanity.com/paper/1902.10730/) (2019)
- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [The Effect of People Recommenders on Echo Chambers and Polarization](https://mlsanity.com/paper/2112.00626/) (2021)
- [Data-driven generation of spatio-temporal routines in human mobility](https://mlsanity.com/paper/1607.05952/) (2016)
- [Future Directions in Human Mobility Science](https://mlsanity.com/paper/2408.00702/) (2024)
- [Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation](https://mlsanity.com/paper/2006.04275/) (2020)
- [Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?](https://mlsanity.com/paper/2108.06973/) (2021)
- [Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison](https://mlsanity.com/paper/1908.00831/) (2019)
- [Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences](https://mlsanity.com/paper/2409.16478/) (2024)
- [How Routing Strategies Impact Urban Emissions](https://mlsanity.com/paper/2207.01456/) (2022)
- [Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks](https://mlsanity.com/paper/2404.03139/) (2024)
- [Coupling Human Mobility and Social Ties](https://mlsanity.com/paper/1502.00690/) (2015)
- [Quantifying the sustainability impact of Google Maps: A case study of Salt Lake City](https://mlsanity.com/paper/2111.03426/) (2021)
- [Trajectory Test-Train Overlap in Next-Location Prediction Datasets](https://mlsanity.com/paper/2203.03208/) (2022)
- [A survey on the impacts of recommender systems on users, items, and human-AI ecosystems](https://mlsanity.com/paper/2407.01630/) (2024)
- [One-Shot Traffic Assignment with Forward-Looking Penalization](https://mlsanity.com/paper/2306.13704/) (2023)
- [Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems](https://mlsanity.com/paper/2408.11565/) (2024)

## 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.