# The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation

arXiv: https://arxiv.org/abs/2008.09273 · HTML: https://mlsanity.com/paper/2008.09273/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2020.

## Built on by (most cited first, 30 of 35)

- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [A Multi-Agent Conversational Recommender System](https://mlsanity.com/paper/2402.01135/) (2024)
- [UP5: Unbiased Foundation Model for Fairness-aware Recommendation](https://mlsanity.com/paper/2305.12090/) (2023)
- [MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation](https://mlsanity.com/paper/2308.11175/) (2023)
- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Large Language Models as Recommender Systems: A Study of Popularity Bias](https://mlsanity.com/paper/2406.01285/) (2024)
- [How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective](https://mlsanity.com/paper/2404.12008/) (2024)
- [Deconfounded Causal Collaborative Filtering](https://mlsanity.com/paper/2110.07122/) (2021)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness](https://mlsanity.com/paper/2110.08353/) (2021)
- [Recommendation Fairness: From Static to Dynamic](https://mlsanity.com/paper/2109.03150/) (2021)
- [Our Model Achieves Excellent Performance on MovieLens: What Does it Mean?](https://mlsanity.com/paper/2307.09985/) (2023)
- [Pre-trained Neural Recommenders: A Transferable Zero-Shot Framework for Recommendation Systems](https://mlsanity.com/paper/2309.01188/) (2023)
- [Fairness Through Domain Awareness: Mitigating Popularity Bias For Music Discovery](https://mlsanity.com/paper/2308.14601/) (2023)
- [Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems](https://mlsanity.com/paper/2312.17443/) (2023)
- [Privacy-Utility-Bias Trade-offs for Privacy-Preserving Recommender Systems](https://mlsanity.com/paper/2511.22515/) (2025)
- [Introducing a Framework and a Decision Protocol to Calibrate Recommender Systems](https://mlsanity.com/paper/2204.03706/) (2022)
- [Exploring and Exploiting Data Heterogeneity in Recommendation](https://mlsanity.com/paper/2305.15431/) (2023)
- [Automatic Feature Fairness in Recommendation via Adversaries](https://mlsanity.com/paper/2309.15418/) (2023)
- [MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels](https://mlsanity.com/paper/2401.12593/) (2024)
- [Interactive Counterfactual Exploration of Algorithmic Harms in Recommender Systems](https://mlsanity.com/paper/2409.06916/) (2024)
- [The Importance of Causality in Decision Making: A Perspective on Recommender Systems](https://mlsanity.com/paper/2410.01822/) (2024)
- [A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems](https://mlsanity.com/paper/2301.07639/) (2023)
- [Recommender Systems: A Primer](https://mlsanity.com/paper/2302.02579/) (2023)
- [A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations](https://mlsanity.com/paper/2303.00400/) (2023)
- [ExCalibR: Expected Calibration of Recommendations](https://mlsanity.com/paper/2304.12311/) (2023)
- [Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics](https://mlsanity.com/paper/2305.18333/) (2023)
- [The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender Systems](https://mlsanity.com/paper/2405.11053/) (2024)
- [Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity](https://mlsanity.com/paper/2505.14310/) (2025)

## Abstract (arXiv)

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.