Measuring Playlist Diversity for Recommendation Systems
Source:
Proceedings of the Audio and Music Computing for Multimedia Workshop in conjunction with ACM Multimedia (2006)
URL:
http://cobweb.ecn.purdue.edu/~malcolm/yahoo/Slaney2006(MeasuringPlaylistDiverisityACMMM).pdf
Abstract:
We describe a way to measure the diversity of consumer’s
musical interests and characterize this diversity using
published musical playlists. For each song in the playlist we
calculate a set of features, which were optimized for genre
recognition, and represent the song as a single point in a
multidimensional genre-space. Given the points for a set of
songs, we fit an ellipsoid to the data, and then describe the
diversity of the playlist by calculating the volume of the
enclosing ellipsoid. We compare 887 different playlists,
representing nearly 29,000 distinct songs, to collections of
different genres and to the size of our entire database.
Playlists tend to be less diverse than a genre, and, by our
measure, about 5 orders of magnitude smaller than the entire
song set. These characteristics are important for
recommendation systems, which want to present users with
a set of recommendations tuned to each user’s diversity.
