On the Incorporation of Psychologically-Driven 'Music' Preference Models for Music Recommendation
On the Incorporation of Psychologically-Driven 'Music' Preference Models for Music Recommendation
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Date
2016-05
Authors
Dalton, Monique Kathryn
Ferraro, Ethan J.
Galuardi, Meg
Robinson, Michael L.
Stauffer, Abigail M.
Walls, Mackenzie Thomas
Advisor
Duraiswami, Ramani
Citation
DRUM DOI
Abstract
There are hundreds of millions of songs available to the public, necessitating the use
of music recommendation systems to discover new music. Currently, such systems
account for only the quantitative musical elements of songs, failing to consider
aspects of human perception of music and alienating the listener’s individual
preferences from recommendations. Our research investigated the relationships
between perceptual elements of music, represented by the MUSIC model, with
computational musical features generated through The Echo Nest, to determine how a
psychological representation of music preference can be incorporated into
recommendation systems to embody an individual’s music preferences. Our resultant
model facilitates computation of MUSIC factors using The Echo Nest features, and
can potentially be integrated into recommendation systems for improved
performance.