  The 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.
