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

Ferraro, Andrés; Bogdanov, Dmitry; Serra, Xavier

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

License

Citation

Ferraro A, Bogdanov D, Serra X. Skip prediction using boosting trees based on acoustic features of tracks in sessions. Paper presented at: 12th ACM International Conference on Web Search and Data Mining; 2019 Feb 11-15; Melbourne, Australia.

Abstract

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.

Description

Comunicació presentada al 12th ACM International Conference on Web Search and Data Mining celebrat del11 al 15 de febrer de 2019 a Melbourne, Austràlia.