# Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data

arXiv: https://arxiv.org/abs/2408.16430 · HTML: https://mlsanity.com/paper/2408.16430/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms](https://mlsanity.com/paper/2011.01731/) (2020)
- [Current Challenges and Visions in Music Recommender Systems Research](https://mlsanity.com/paper/1710.03208/) (2017)
- [The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study](https://mlsanity.com/paper/1912.04696/) (2019)
- [A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps](https://mlsanity.com/paper/2106.03819/) (2021)
- [Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?](https://mlsanity.com/paper/2108.06973/) (2021)
- [Track Mix Generation on Music Streaming Services using Transformers](https://mlsanity.com/paper/2307.03045/) (2023)
- [A Stakeholder-Centered View on Fairness in Music Recommender Systems](https://mlsanity.com/paper/2209.06126/) (2022)
- [Risk Scores, Label Bias, and Everything but the Kitchen Sink](https://mlsanity.com/paper/2305.12638/) (2023)
- [Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems](https://mlsanity.com/paper/1912.06933/) (2019)
- [Follow the guides: disentangling human and algorithmic curation in online music consumption](https://mlsanity.com/paper/2109.03915/) (2021)
- [Artist and style exposure bias in collaborative filtering based music recommendations](https://mlsanity.com/paper/1911.04827/) (2019)

## Built on by (most cited first, 2 of 2)

- [Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation](https://mlsanity.com/paper/2511.16478/) (2025)
- [Modeling Musical Genre Trajectories through Pathlet Learning](https://mlsanity.com/paper/2505.03480/) (2025)

## Abstract (arXiv)

This paper examines the influence of recommender systems on local music representation, discussing prior findings from an empirical study on the LFM-2b public dataset. This prior study argued that different recommender systems exhibit algorithmic biases shifting music consumption either towards or against local content. However, LFM-2b users do not reflect the diverse audience of music streaming services. To assess the robustness of this study's conclusions, we conduct a comparative analysis using proprietary listening data from a global music streaming service, which we publicly release alongside this paper. We observe significant differences in local music consumption patterns between our dataset and LFM-2b, suggesting that caution should be exercised when drawing conclusions on local music based solely on LFM-2b. Moreover, we show that the algorithmic biases exhibited in the original work vary in our dataset, and that several unexplored model parameters can significantly influence these biases and affect the study's conclusion on both datasets. Finally, we discuss the complexity of accurately labeling local music, emphasizing the risk of misleading conclusions due to unreliable, biased, or incomplete labels. To encourage further research and ensure reproducibility, we have publicly shared our dataset and code.