An analysis of global and regional mainstreaminess for personalized music recommender systems

The \emph{music mainstreaminess of a listener} reflects how strong a person's listening preferences correspond to those of the larger population. Considering that music mainstream may be defined from different perspectives, we show country-specific differences and study how taking into account music mainstreaminess influences the quality of music recommendations. In this paper, we first propose 11 novel mainstreaminess measures characterizing music listeners, considering both a global and a country-specific basis for mainstreaminess. To this end, we model \emph{preference profiles} (as a vector over artists) for users, countries, and globally, incorporating artist frequency, listener frequency, and a newly proposed TF-IDF-inspired weighting function, which we call artist frequency--inverse listener frequency (AF-ILF). The resulting preference profile for each user $u$ is then related to the respective country-specific and global preference profile using fraction-based approaches, symmetrized Kullback-Leibler divergence, and Kendall's $\tau$ rank correlation, in order to quantify $u$'s mainstreaminess. Second, we detail country-specific peculiarities concerning what defines the countries' mainstream and discuss the proposed mainstreaminess definitions. Third, we show that incorporating the proposed global and country-specific mainstreaminess measures into the music recommendation process can notably improve accuracy of rating prediction.