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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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“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]
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“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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Cites
Finite-time Analysis of the Multiarmed Bandit Problem
Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002) · Machine Learning · doi:10.1023/a:1013689704352 · Cited by 6,033
Deep Neural Networks for YouTube Recommendations
Covington, P., Adams, J., & Sargin, E. (2016) · Proceedings of the 10th ACM Conference on Recommender Systems · doi:10.1145/2959100.2959190 · Cited by 3,539
Exposure to ideologically diverse news and opinion on Facebook
Bakshy, E., Messing, S., & Adamic, L. A. (2015) · Science · doi:10.1126/science.aaa1160 · Cited by 3,245
THOMPSON, W. R. (1933) · Biometrika · doi:10.1093/biomet/25.3-4.285 · Cited by 2,712
Filter Bubbles, Echo Chambers, and Online News Consumption
Flaxman, S., Goel, S., & Rao, J. M. (2016) · Public Opinion Quarterly · doi:10.1093/poq/nfw006 · Cited by 1,992
FILTER BUBBLE: Wie wir im Internet entmündigt werden
Pariser, E. (2012) · doi:10.3139/9783446431164 · Cited by 1,849
Recommender system application developments: A survey
Jie Lu, B., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.03.008 · Cited by 1,584
E-Commerce Recommendation Applications
Schafer, J. B., Konstan, J. A., & Riedl, J. (2001) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009804230409 · Cited by 1,534
Tweeting From Left to Right: Is Online Political Communication More Than an Echo Chamber?
Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015) · Psychological Science · doi:10.1177/0956797615594620 · Cited by 1,472
Lattimore, T., & Szepesvári, C. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108571401 · Cited by 1,196
Self-citation
Research works printed but not linked: 2
- E. Pariser . 2011. The Filter Bubble: What the Internet is Hiding from You . Penguin UK. E. Pariser. 2011. The Filter Bubble: What the Internet is Hiding from You .Penguin UK.
- C. R. Sunstein. 2009. Republich.com 2.0 .Princeton University Press. C. R. Sunstein. 2009. Republich.com 2.0 .Princeton University Press.
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Metadata
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › 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
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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.
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