Distance- and Rank-based Music Mainstreaminess Measurement

Abstract

A music listener’s mainstreaminess indicates the extent to which her listening preferences correspond to those of the population at large. However, formal definitions to quantify the level of mainstreaminess of a listener are rare and those available define mainstreaminess based on fractions between some kind of individual and global listening profiles. We argue, in contrast, that measures based on a modified version of the well-established Kullback-Leibler (KL) divergence as well as rank-order correlation coefficient may be better suited to capture the mainstreaminess of listeners. We therefore propose two measures adopting KL divergence and rank-order correlation and show, on a real-world dataset of over one billion user-generated listening events (LFM-1b), that music recommender systems can notably benefit when grouping users according to their level of mainstreaminess with respect to these two measures. This particularly holds for the frequently neglected listener group which is characterized by low mainstreaminess.

Publication
2nd Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems, in conjunction with 25th International Conference on User Modeling, Adaptation and Personalization (UMAP 2017)