Inproceedings,
Introducing global and regional mainstreaminess for improving personalized music recommendation
15th International Conference on Advances in Mobile Computing & Multimedia (MoMM2017), page 74-81. New York, NY, USA, ACM, (December 2017)Best Paper Award.
DOI: 10.1145/3151848.3151849
Abstract
The music mainstreaminess of a user reflects how strong a user's listening preferences correspond to those of the larger population. Considering that music mainstream may be defined from different perspectives and on various levels, e.g.,~geographical (charts of a country), genre (``Indie charts"), or distribution channel (radio charts vs.~download charts), we study how the user's music mainstreaminess influences the quality of music recommendations. The paper's contribution is three-fold.
First, we propose 11 novel mainstreaminess measures characterizing music listeners, considering both a global and a country-specific basis. To this end, we model 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 demonstrate country-specific peculiarities of these 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.
