# The Impact of Popularity Bias on Fairness and Calibration in Recommendation

arXiv: https://arxiv.org/abs/1910.05755 · HTML: https://mlsanity.com/paper/1910.05755/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2019.

## Built on by (most cited first, 9 of 9)

- [How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective](https://mlsanity.com/paper/2404.12008/) (2024)
- [FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings](https://mlsanity.com/paper/2205.00313/) (2022)
- [Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability](https://mlsanity.com/paper/2107.00833/) (2021)
- [Factual and Personalized Recommendations using Language Models and Reinforcement Learning](https://mlsanity.com/paper/2310.06176/) (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)
- [Datasets for Navigating Sensitive Topics in Recommendation Systems](https://mlsanity.com/paper/2509.07269/) (2025)
- [A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations](https://mlsanity.com/paper/2303.00400/) (2023)
- [Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems](https://mlsanity.com/paper/2605.02379/) (2026)
- [Why This, Not That? Mining User Profiles for Pair-wise Counterfactuals](https://mlsanity.com/paper/2608.21662/) (2026)

## 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. A well-known type of bias in recommendation is popularity bias where few popular items are over-represented in recommendations, while the majority of other items do not get significant exposure. We conjecture that popularity bias is one important factor leading to miscalibration in recommendation. Our experimental results using two real-world datasets show that there is a strong correlation between how different user groups are affected by algorithmic popularity bias and their level of interest in popular items. Moreover, we show algorithms with greater popularity bias amplification tend to have greater miscalibration.