Papers › Skip prediction using boosting trees based on acoustic features of tracks in sessions
Skip prediction using boosting trees based on acoustic features of tracks in sessions
Andrés Ferraro, Dmitry Bogdanov, Xavier Serra
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections