Meaningful Classification for Music Recommendation Systems

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cityu.schoolSchool of Technology and Computing
cityu.siteSeattle
cityu.site.countryUnited States
dc.contributor.authorGeving, Brian
dc.date.accessioned2021-12-23T22:08:25Z
dc.date.available2021-12-23T22:08:25Z
dc.date.issued2021-12
dc.description.abstractWhile the field of Music Information Retrieval (MIR) is steadily progressing, the majority of music recommendation systems still operate using collaborative filtering. Collaborative filtering operates by finding patterns between music represented in collected playlists. While this method works well for music that has a large amount of representation, it is a very poor method for recommending music that has very little availability. By utilizing machine learning classification algorithms and low-level audio feature extraction, there exists a method to classify music with specific parameters. Instead of using modern music genres as a way to relate music, using labels provided by collaborative filtering can provide a high accuracy way to recommend similar music with low representation.
dc.identifier.urihttp://hdl.handle.net/20.500.11803/1649
dc.language.isoen
dc.publisher.institutionCity University of Seattle (CityU)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States
dc.rightsopenAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/
dc.subjectSVM
dc.subjectclassification
dc.subjectmusic
dc.subjectLibrosa
dc.subjectMir
dc.subjectmachine learning
dc.subjectcollaborative filtering
dc.titleMeaningful Classification for Music Recommendation Systems
dc.typeCapstone
thesis.degree.disciplineComputer Science
thesis.degree.grantorCity University of Seattle
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
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