Music Mood Classification algorithms for recommending new music to users. However, at the
core of their recommendations is the collaborative filtering algorithm,
which recommends music based on what other people with similar
tastes have listened to [1]. While this can produce highly relevant
content recommendations, it tends to promote only popular content
[2]. The popularity bias inherent in collaborative-filtering based
systems can overlook music that fits a user’s taste, simply because
nobody else is listening to it. One possible solution to this problem is
to recommend music based on features of the music itself, and
recommend songs which have similar features. Here, a method for
extracting high-level features representing the mood of a song is
presented, with the aim of tailoring music recommendations to an
individual's mood, and providing music recommendations with
diversity in popularity.]]>autGomez, Luis AngelthsKevin, BurgerdgcAlberto, HernándezctbArts, Media and Engineering Sch TctbComputer Science and Engineering ProgramctbBarrett, The Honors Collegeenghttps://hdl.handle.net/2286/R.I.5653512 pages115874452071628716197131461lagomez8In Copyright2020-05TextMusic ClassificationMusic Information Retrievaldeep learningSignal Analysis