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dc.contributor.authorRobinson, Kyle
dc.date.accessioned2021-02-17 15:26:22 (GMT)
dc.date.available2021-02-17 15:26:22 (GMT)
dc.date.issued2021-02-17
dc.date.submitted2021-02-10
dc.identifier.urihttp://hdl.handle.net/10012/16808
dc.description.abstractWhile many researchers have proposed various ways of quantifying recommendation list diversity, these approaches have had little input from users on their own perceptions and preferences in seeking diversity. Through a set of user studies we provide a better understanding of how users view the concept of diversity in music recommendations, and how intra-list diversity can be adapted to better represent their diversity preference. Our results show that users have a clear idea of what music recommendation diversity means to them, accuracy metrics do not model overall list satisfaction, and filtering recommendations on genre before list diversification can positively impact list satisfaction. More importantly, our results highlight the need to base music recommendation metrics on insights from real peopleen
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectmusic information retrievalen
dc.subjectrecommender systemsen
dc.subjecthuman-computer interactionen
dc.subjectmusic recommender systemsen
dc.subjectrecommender system diversityen
dc.subjectbeyond accuracy metricsen
dc.subjectinformation retrievalen
dc.subjectmusicen
dc.titlePeople know how diverse their music recommendations should be; why don’t we?en
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentDavid R. Cheriton School of Computer Scienceen
uws-etd.degree.disciplineComputer Scienceen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Mathematicsen
uws-etd.embargo.terms0en
uws.contributor.advisorBrown, Daniel
uws.contributor.affiliation1Faculty of Mathematicsen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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