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 ...
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.
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