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

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dc.contributor.advisorDuraiswami, Ramani
dc.contributor.authorDalton, Monique Kathryn
dc.contributor.authorFerraro, Ethan J.
dc.contributor.authorGaluardi, Meg
dc.contributor.authorRobinson, Michael L.
dc.contributor.authorStauffer, Abigail M.
dc.contributor.authorWalls, Mackenzie Thomas
dc.date.accessioned2016-06-10T13:20:08Z
dc.date.available2016-06-10T13:20:08Z
dc.date.issued2016-05
dc.description.abstractThere 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.en_US
dc.identifierhttps://doi.org/10.13016/M2D762
dc.identifier.urihttp://hdl.handle.net/1903/18085
dc.language.isoen_USen_US
dc.relation.isAvailableAtDigital Repository at the University of Maryland
dc.relation.isAvailableAtGemstone Program, University of Maryland (College Park, Md)
dc.subjectGemstone Team MUSICen_US
dc.subjectmusic recommendation systemsen_US
dc.subjectmusic preferenceen_US
dc.subjectThe Echo Nesten_US
dc.subjectMUSIC modelen_US
dc.titleOn the Incorporation of Psychologically-Driven 'Music' Preference Models for Music Recommendationen_US
dc.typeThesisen_US

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