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

TL;DR

This paper introduces measures to quantify how much a user's music preferences align with mainstream music, analyzing global and country-specific differences, and demonstrating their utility in improving personalized music recommendations.

Contribution

It defines new quantitative measures of mainstreaminess at global and country levels, and evaluates their effectiveness in enhancing music recommendation accuracy.

Findings

Substantial country-specific differences in music preferences.

Different mainstreaminess measures have varying benefits for prediction accuracy.

Country-level analysis reveals outliers with higher or lower mainstreamness than global averages.

Abstract

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.