Automatic Music Recommendation Systems: Do Demographic, Profiling, and Contextual Features Improve Their Performance?

@inproceedings{Vigliensoni2016AutomaticMR,
  title={Automatic Music Recommendation Systems: Do Demographic, Profiling, and Contextual Features Improve Their Performance?},
  author={Gabriel Vigliensoni and Ichiro Fujinaga},
  booktitle={ISMIR},
  year={2016}
}
Traditional automatic music recommendation systems’ performance typically rely on the accuracy of statistical models learned from past preferences of users on music items. However, additional sources of data such as demographic attributes of listeners, their listening behaviour, and their listening contexts encode information about listeners, and their listening habits, that may be used to improve the accuracy of music recommendation models. In this paper we introduce a large dataset of music… CONTINUE READING
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