Personalized Indexing of Music by Emotions
1 online resource (92 pages) : PDF
University of North Carolina at Charlotte
How a person interprets music and what prompts a person to feel certain emotions are two very subjective things. This dissertation presents a method where a system can learn and track a user's listening habits with the purpose of recommending songs that fits the user's specific way of interpreting music and emotions. First a literature review is presented which shows an overview of the current state of recommender systems, as well as describing classifiers; then the process of collecting user data isdiscussed; then the process of training and testing personalized classifiers is described; finally a system combining the personalized classifiers with clustered data into ahierarchy of recommender systems is presented.
Wu, WenshengYang, JingWieczorkowska, AlicjaHaldeman, Randal
Thesis (Ph.D.)--University of North Carolina at Charlotte, 2014.
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