# The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study

arXiv: https://arxiv.org/abs/1912.04696 · HTML: https://mlsanity.com/paper/1912.04696/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

## Built on by (most cited first, 24 of 24)

- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [User-centered Evaluation of Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2103.06364/) (2021)
- [Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers](https://mlsanity.com/paper/2406.14325/) (2024)
- [LLMRec: Benchmarking Large Language Models on Recommendation Task](https://mlsanity.com/paper/2308.12241/) (2023)
- [Reproducibility in Machine Learning-Driven Research](https://mlsanity.com/paper/2307.10320/) (2023)
- [A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems](https://mlsanity.com/paper/2107.03415/) (2021)
- [The Unfairness of Popularity Bias in Book Recommendation](https://mlsanity.com/paper/2202.13446/) (2022)
- [Experiments on Generalizability of User-Oriented Fairness in Recommender Systems](https://mlsanity.com/paper/2205.08289/) (2022)
- [Establishing and Evaluating Trustworthy AI: Overview and Research Challenges](https://mlsanity.com/paper/2411.09973/) (2024)
- [The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation](https://mlsanity.com/paper/2202.13307/) (2022)
- [Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems](https://mlsanity.com/paper/2203.00376/) (2022)
- [A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services](https://mlsanity.com/paper/2304.09061/) (2023)
- [Metrics for popularity bias in dynamic recommender systems](https://mlsanity.com/paper/2310.08455/) (2023)
- [Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data](https://mlsanity.com/paper/2408.16430/) (2024)
- [Exploring Popularity Bias in Music Recommendation Models and Commercial Steaming Services](https://mlsanity.com/paper/2208.09517/) (2022)
- [Multiple Key-value Strategy in Recommendation Systems Incorporating Large Language Model](https://mlsanity.com/paper/2310.16409/) (2023)
- [The Importance of Causality in Decision Making: A Perspective on Recommender Systems](https://mlsanity.com/paper/2410.01822/) (2024)
- [Prompt-Based LLMs for Position Bias-Aware Reranking in Personalized Recommendations](https://mlsanity.com/paper/2505.04948/) (2025)
- [Hidden Author Bias in Book Recommendation](https://mlsanity.com/paper/2209.00371/) (2022)
- [A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations](https://mlsanity.com/paper/2303.00400/) (2023)
- [Knowledge-based Multimodal Music Similarity](https://mlsanity.com/paper/2306.12249/) (2023)
- [How to Surprisingly Consider Recommendations? A Knowledge-Graph-based Approach Relying on Complex Network Metrics](https://mlsanity.com/paper/2405.08465/) (2024)
- [Online and Offline Evaluations of Collaborative Filtering and Content Based Recommender Systems](https://mlsanity.com/paper/2411.01354/) (2024)
- [Modeling Musical Genre Trajectories through Pathlet Learning](https://mlsanity.com/paper/2505.03480/) (2025)

## Abstract (arXiv)

Research has shown that recommender systems are typically biased towards popular items, which leads to less popular items being underrepresented in recommendations. The recent work of Abdollahpouri et al. in the context of movie recommendations has shown that this popularity bias leads to unfair treatment of both long-tail items as well as users with little interest in popular items. In this paper, we reproduce the analyses of Abdollahpouri et al. in the context of music recommendation. Specifically, we investigate three user groups from the LastFM music platform that are categorized based on how much their listening preferences deviate from the most popular music among all LastFM users in the dataset: (i) low-mainstream users, (ii) medium-mainstream users, and (iii) high-mainstream users. In line with Abdollahpouri et al., we find that state-of-the-art recommendation algorithms favor popular items also in the music domain. However, their proposed Group Average Popularity metric yields different results for LastFM than for the movie domain, presumably due to the larger number of available items (i.e., music artists) in the LastFM dataset we use. Finally, we compare the accuracy results of the recommendation algorithms for the three user groups and find that the low-mainstreaminess group significantly receives the worst recommendations.