Degenerate Feedback Loops in Recommender Systems
Open accessAbstract
Machine learning is used extensively in recommender systems deployed in products. The decisions made by these systems can influence user beliefs and preferences which in turn affect the feedback the learning system receives - thus creating a feedback loop. This phenomenon can give rise to the so-called "echo chambers" or "filter bubbles" that have user and societal implications. In this paper, we provide a novel theoretical analysis that examines both the role of user dynamics and the behavior of recommender systems, disentangling the echo chamber from the filter bubble effect. In addition, we offer practical solutions to slow down system degeneracy. Our study contributes toward understanding and developing solutions to commonly cited issues in the complex temporal scenario, an area that is still largely unexplored.
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Uncontrolled biases in recommender systems can lead to uneven distribution of opportunities and resources, exacerbating echo chamber or filter bubble effects.
“Usually, a biased system generates the biased output, and the uncontrolled biases can lead to the uneven distribution of opportunities and resources [58, 78], exacerbating the “ 𝐸𝑐ℎ𝑜 𝐶ℎ𝑎𝑚𝑏𝑒𝑟 ” or “ 𝐹𝑖𝑙𝑡𝑒𝑟 𝐵𝑢𝑏𝑏𝑙𝑒𝑠 ” effects [86, 87].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [87]
User engagement is a signal of value to both users and platforms deploying algorithms, connecting them in complex feedback loops.
“It is important to note that user engagement is a signal of value to both users and platforms deploying algorithms, connecting them in complex feedback loops 144,145 :”
From Machine culture (2023) · cites this paper as [145]
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Biasio, A. D., Monaro, M., Oneto, L., Ballan, L., & Navarin, N. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110699 · Cited by 12 · see citing works
A Causal View for Item-level Effect of Recommendation on User Preference
Cai, W., Feng, F., Wang, Q., Tian, Y., Liu, Z., & Xu, C. (2023) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3539597.3570461 · Cited by 11 · see citing works
T-RECS: A Simulation Tool to Study the Societal Impact of Recommender SystemsPreprint
Lucherini, E., Sun, M. D., Winecoff, A., & Narayanan, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.08959 · Cited by 11 · see citing works
AI and the Future of Disinformation Campaigns: Part 2: A Threat Model
Sedova, K., McNeill, C., Johnson, A., Joshi, A. A., & Wulkan, I. (2021) · doi:10.51593/2021ca011 · Cited by 10 · see citing works
Flexibly manipulating popularity bias for tackling trade-offs in recommendation
Okamura, H., Maeda, K., Togo, R., Ogawa, T., & Haseyama, M. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103606 · Cited by 9 · see citing works
Castells, P., & Jannach, D. (2024) · ACM eBooks · doi:10.1145/3674127.3674131 · Cited by 9 · see citing works
Performative Prediction: Past and Future
Hardt, M., & Mendler-Dünner, C. (2025) · Statistical Science · doi:10.1214/25-sts986 · Cited by 9 · see citing works
AI Facilitated Isolations? The Impact of Recommendation-based Influence Diffusion in Human Society
Hu, Y., Wu, S., Jiang, C., Li, W., Bai, Q., & Roehrer, E. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/705 · Cited by 9 · see citing works
De-sounding echo chambers: Simulation-based analysis of polarization dynamics in social networks
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Treatment Effect Estimation for User Interest Exploration on Recommender Systems
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Harm Mitigation in Recommender Systems under User Preference Dynamics
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Artificial intelligence in teaching social disciplines: Opportunities and challenges of tools
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Keyword-enhanced recommender system based on inductive graph matrix completion
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Spiral of Silence and Its Application in Recommender Systems
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An investigation of the exposure effect of recommender systems in hospitality
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A Value-centered Exploration of Data Privacy and Personalized Privacy Assistants
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Counterfactual Graph Convolutional Learning for Personalized Recommendation
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Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation
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Open-Domain Trending Hashtag Recommendation for Videos
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- Authors
- Jiang, R., Chiappa, S., Lattimore, T., György, A., & Kohli, P.
- Affiliations
- Google DeepMind (United Kingdom) (GB)
- Venue
- Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society
- Publisher
- ACM
- Published
- 2019-01-27
- Pages
- 383-390
- ISSN
- 3065-8365 (ISSN-L)
- OpenAlex
- W2917760808
- Cited by
- 232
- References
- 15
- Citations by year
- 2026: 37 · 2025: 23 · 2024: 46 · 2023: 34 · 2022: 34 · 2021: 30 · 2020: 20 · 2019: 8
- FWCI
- 18.17
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings of the AAAI/ACM Conference on AI Ethics and Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- recommender systems, feedback loops, degeneracy, echo chambers, filter bubbles, user dynamics, temporal dynamics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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