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

Date Published: June 7, 2019

Publisher: Public Library of Science

Author(s): Christine Bauer, Markus Schedl, Chi Ho Yeung.


Popularity-based approaches are widely adopted in music recommendation systems, both in industry and research. These approaches recommend to the target user what is currently popular among all users of the system. 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 preferences far away from the global music mainstream. Addressing this gap, the contribution of this article is three-fold.

First, we provide several quantitative measures describing the proximity of a user’s music preference to the music mainstream. Assuming that there is a difference between the global music mainstream and a country-specific one, 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. To quantify such music preferences, we define a music item’s popularity in terms of artist playcounts (APC) and artist listener counts (ALC). Moreover, we adopt a distribution-based and a rank-based approach as means to decrease bias towards the head of the long-tail distribution. This eventually results in a framework of 6 measures to quantify music mainstream.

Second, we perform in-depth quantitative and qualitative studies of music mainstream in that we (i) analyze differences between countries in terms of their level of mainstreaminess, (ii) uncover both positive and negative outliers (substantially higher and lower country-specific popularity, respectively, compared to the global mainstream), analyzing these with a mixed-methods approach, and (iii) investigate differences between countries in terms of listening preferences related to popular music artists. We conduct our studies and experiments using the standardized LFM-1b dataset, from which we analyze about 800,000,000 listening events shared by about 53,000 users (from 47 countries) of the music streaming platform We show that there are substantial country-specific differences in listeners’ music consumption behavior with respect to the most popular artists listened to.

Third, we demonstrate the applicability of our study results to improve music recommendation systems. To this end, 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: defined by a distribution-based or rank-based approach, defined on a global level or on a country level (for the user’s country), and for APC or ALC. Our approach roughly equals a hybrid recommendation approach in which a demographic filtering strategy is implemented before collaborative filtering is performed. Results suggest that, in terms of rating prediction accuracy, each of the presented mainstreaminess definitions has its merits.

Partial Text

Nowadays, user-generated content is abundantly available online and the amount of available content increases tremendously on a daily basis [1]. Such consumable content is versatile and includes, for instance, news, videos, music, and photographs. The opportunity to access a large amount of content also has its downsides, because it frequently leads to information [2] or choice overload [3] and people do not know what to choose or do not find the content that they are interested in. Thus, recommender systems (also known as recommendation systems) have become important tools because they assists users in searching, sorting, and filtering the massive amount of any kind of online content [4]. As a result, they help decreasing the information and choice overload problems, too. Recommender systems play an important role in people’s everyday life and support versatile activities such as shopping [5–7] or consuming news [1], movies [8, 9], and music [10–12].

This research foremost connects to the area of music recommendation systems as well as to the aspects of popularity and mainstream in the music domain, and their consideration in recommendation systems. We provide a conceptual foundation and discuss related work about the former in Section Music recommendation systems, about the latter in Section Music popularity and mainstreaminess and Section Popularity and mainstreaminess in music recommendation, respectively.

In the work at hand, we follow a three-step approach. First, we provide several measures to quantify the mainstreaminess in a user’s or country’s music listening behavior, in relation to both a global and a country-specific mainstream. We extend our previous work [56, 60] in that we streamline our mainstreaminess framework, and define and investigate distribution- and rank-based mainstreaminess measures on artist playcounts (APC) as well as on artist listener counts (ALC). We further normalize the distribution-based approaches for better comparison.

We present and discuss the results of our research in three subsections: (i) We provide an overview of descriptive statistics concerning the different mainstreaminess definitions that were presented in Section Differences in mainstreaminess levels between countries (Section Overview of results on the mainstreaminess definitions). (ii) We present and discuss the results of our investigations on country-specific differences of users’ listening behavior concerning music mainstreaminess (Section Country-specific music mainstreaminess). (iii) Motivated by these results, we subsequently show how tailoring recommendations to country-specific characteristics of mainstreaminess may yield improved recommendation results (Section Exploiting mainstream and country information for music recommendation).

Popularity-based approaches are widely adopted in music recommendation systems, both in industry and research. However, as the popularity distribution of music items is typically a long-tail distribution, current approaches to music recommendations fall short in satisfying listeners that have specialized music preferences far away from the global music mainstream.