The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation
Open accessAbstract
Recently there has been a growing interest in fairness-aware recommender systems including fairness in providing consistent performance across different users or groups of users. A recommender system could be considered unfair if the recommendations do not fairly represent the tastes of a certain group of users while other groups receive recommendations that are consistent with their preferences. In this paper, we use a metric called miscalibration for measuring how a recommendation algorithm is responsive to users’ true preferences and we consider how various algorithms may result in different degrees of miscalibration for different users. In particular, we conjecture that popularity bias which is a well-known phenomenon in recommendation is one important factor leading to miscalibration in recommendation. Our experimental results using two real-world datasets show that there is a connection between how different user groups are affected by algorithmic popularity bias and their level of interest in popular items. Moreover, we show that the more a group is affected by the algorithmic popularity bias, the more their recommendations are miscalibrated.
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Current recommendation systems appear to disproportionately amplify high-popularity accounts, creating inequality in exposure that may result in less personalized and miscalibrated recommendations for certain user groups.
“Current recommendation systems appear to disproportionately amplify high-popularity accounts, creating inequality in exposure that may result in less personalized and miscalibrated recommendations for certain user groups [1].”
From Auditing Political Exposure Bias: Algorithmic Amplification on Twitter/X During the 2024 U.S. Presidential Election (2025) · cites this paper as [1]
Section 3.2 (page not supplied) says algorithms often amplify popularity bias by overrecommending popular items, including to users interested in niche items. Section 5.1 links this bias to unequal effects and miscalibration across user groups. The paper discusses items, not high-popularity accounts, and does not establish unequal exposure for accounts. (LLM, conf=0.88)
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AMBAR: A dataset for Assessing Multiple Beyond-Accuracy Recommenders
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Computational Versus Perceived Popularity Miscalibration in Recommender Systems
Lesota, O., Escobedo, G., Deldjoo, Y., Ferwerda, B., Kopeinik, S., Lex, E., Rekabsaz, N., & Schedl, M. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591964 · Cited by 7 · see citing works
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
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Recommender Systems, Ground Truth, and Preference Pollution
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Toward personalized AI-powered recommender systems to support users’ daily music choice experiences
Shin, Y., Ranjan, K., Kowalski, M. C., & Yoon, J. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103574 · Cited by 6 · see citing works
Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation
Lin, K., Mansoury, M., Eskandanian, F., Sabouri, M., & Mobasher, B. (2024) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3631700.3664869 · Cited by 5 · see citing works
Self-citationFine-tuning for Inference-efficient Calibrated Recommendations
Lesota, O., Bajko, A., Walder, M., Wenzel, M., Tommasel, A., & Schedl, M. (2025) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3705328.3759319 · Cited by 5 · see citing works
Reconciling the Quality vs Popularity Dichotomy in Online Cultural Markets
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MIN: Multi-stage Interactive Network for Multimodal Recommendation
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Recommendation Fairness: From Static to DynamicPreprint
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Collaborative filtering algorithms are prone to mainstream-taste bias
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MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels
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- Authors
- Abdollahpouri, H., Mansoury, M., Burke, R., & Mobasher, B.
- Affiliations
- University of Colorado Boulder (US)
- Eindhoven University of Technology (NL)
- DePaul University (US)
- Venue
- Fourteenth ACM Conference on Recommender Systems
- Publisher
- ACM
- Published
- 2020-09-22
- Pages
- 726-731
- OpenAlex
- W3080925373
- Cited by
- 183
- References
- 28
- Citations by year
- 2026: 24 · 2025: 27 · 2024: 56 · 2023: 34 · 2022: 25 · 2021: 17
- FWCI
- 14.41
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Conference on Recommender Systems (RecSys) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National science foundation (1911025)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Keywords
- popularity bias, calibration error, recommendation fairness, recommender systems, user preference representation
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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