# Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems

arXiv: https://arxiv.org/abs/1912.06933 · HTML: https://mlsanity.com/paper/1912.06933/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems](https://mlsanity.com/paper/2203.00376/) (2022)
- [What We Know So Far: Artificial Intelligence in African Healthcare](https://mlsanity.com/paper/2305.18302/) (2023)
- [Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data](https://mlsanity.com/paper/2408.16430/) (2024)
- [Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation](https://mlsanity.com/paper/2511.16478/) (2025)

## Abstract (arXiv)

Popularity-based approaches are widely adopted in music recommendation systems, both in industry and research. However, as the popularity distribution of music items typically is a long-tail distribution, popularity-based approaches to music recommendation fall short in satisfying listeners that have specialized music. The contribution of this article is three-fold. We provide several quantitative measures describing the proximity of a user's music preference to the music mainstream. We define the measures at two levels: relating a listener's music preferences to the global music preferences of all users, or relating them to music preferences of the user's country. Moreover, we adopt a distribution-based and a rank-based approach as means to decrease bias towards the head of the long-tail distribution. We analyze differences between countries in terms of their level of mainstreaminess, uncover both positive and negative outliers (substantially higher and lower country-specific popularity, respectively, compared to the global mainstream), and investigate differences between countries in terms of listening preferences related to popular music artists. We use the standardized LFM-1b dataset, from which we analyze about 8 million listening events shared by about 53,000 users (from 47 countries) of the music streaming platform Last.fm. We show that there are substantial country-specific differences in listeners' music consumption behavior with respect to the most popular artists listened to. We conduct rating prediction experiments in which we tailor recommendations to a user's level of preference for the music mainstream using the proposed 6 mainstreaminess measures. Results suggest that, in terms of rating prediction accuracy, each of the presented mainstreaminess definitions has its merits.