Music recommendation system based on usage history and automatic genre classification

The personalized music recommender supports the user-favorite songs stored in a huge music database. In order to predict only user-favorite songs, managing user preferences information and genre classification are necessary. In our study, a very short feature vector, obtained from low dimensional pr...

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Bibliographic Details
Published in2015 IEEE International Conference on Consumer Electronics (ICCE) pp. 134 - 135
Main Authors Jongseol Lee, Saim Shin, Dalwon Jang, Sei-Jin Jang, Kyoungro Yoon
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2015
Subjects
Engines
Feature extraction
Generators
History
Recommender systems
Support vector machine classification
Vectors
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Summary:The personalized music recommender supports the user-favorite songs stored in a huge music database. In order to predict only user-favorite songs, managing user preferences information and genre classification are necessary. In our study, a very short feature vector, obtained from low dimensional projection and already developed audio features, is used for music genre classification problem. We applied a distance metric learning algorithm in order to reduce the dimensionality of feature vector with a little performance degradation. We propose the system about the automatic management of the user preferences and genre classification in the personalized music system.
ISSN:2158-3994
2158-4001
DOI:10.1109/ICCE.2015.7066352