Dipartimento di Ingegneria informatica, automatica e gestionale

Grouping songs together, according to music preferences, mood or other characteristics, is an activity which reflects personal listening behaviours and tastes. In the last two decades, due to the increasing size of music catalogue accessible and to improvements of recommendation algorithms, people have been exposed to new ways for creating playlists. In this work, through the statistical analysis of more than 400K playlists from four datasets, created in different temporal and technological contexts, we aim to understand if it is possible to extract information about the evolution of humans strategies for playlist creation. We focus our analysis on two driving concepts of the Music Information Retrieval literature: popularity and diversity.