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Popularity-based approaches are widely adopted in music recommendationsystems, both in industry and research. However, as the popularity distributionof music items typically is a long-tail distribution, popularity-basedapproaches to music recommendation fall short in satisfying listeners that havespecialized music. The contribution of this article is three-fold. We provideseveral quantitative measures describing the proximity of a user's musicpreference to the music mainstream. We define the measures at two levels:relating a listener's music preferences to the global music preferences of allusers, 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 decreasebias towards the head of the long-tail distribution. We analyze differencesbetween countries in terms of their level of mainstreaminess, uncover bothpositive and negative outliers (substantially higher and lower country-specificpopularity, respectively, compared to the global mainstream), and investigatedifferences between countries in terms of listening preferences related topopular music artists. We use the standardized LFM-1b dataset, from which weanalyze about 8 million listening events shared by about 53,000 users (from 47countries) of the music streaming platform Last.fm. We show that there aresubstantial country-specific differences in listeners' music consumptionbehavior with respect to the most popular artists listened to. We conductrating prediction experiments in which we tailor recommendations to a user'slevel of preference for the music mainstream using the proposed 6mainstreaminess measures. Results suggest that, in terms of rating predictionaccuracy, each of the presented mainstreaminess definitions has its merits.

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