Skip prediction using boosting trees based on acoustic features of tracks in sessions

TitleSkip prediction using boosting trees based on acoustic features of tracks in sessions
Publication TypeConference Paper
Year of Publication2019
Conference NameWSDM Cup Workshop 2019
AuthorsFerraro, A., Bogdanov D., & Serra X.
AbstractThe Spotify Sequential Skip Prediction Challenge focuses on predicting if a track in a session will be skipped by the user or not. In this paper, we describe our approach to this problem and the final system that was submitted to the challenge by our team from the Music Technology Group (MTG) under the name ``aferraro''. This system consists in combining the predictions of multiple boosting trees models trained with features extracted from the sessions and the tracks. The proposed approach achieves good overall performance (MAA of 0.554), with our model ranked 14th out of more than 600 submissions in the final leaderboard.
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