Note:
This bibliographic page is archived and will no longer be updated.
For an up-to-date list of publications from the Music Technology Group see the
Publications list
.
Skip prediction using boosting trees based on acoustic features of tracks in sessions
| Title | Skip prediction using boosting trees based on acoustic features of tracks in sessions |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Conference Name | WSDM Cup Workshop 2019 |
| Authors | Ferraro, A. , Bogdanov D. , & Serra X. |
| 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. |
| preprint/postprint document | http://arxiv.org/abs/1903.11833 |
