Think Deeper; Share Often

2016 3rd Place Award

On the Incorporation of Psychologically-Driven 'Music' Preference Models for Music Recommendation

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.