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

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• dc.contributor.author Ferraro, Andrés
• dc.contributor.author Bogdanov, Dmitry
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2021-05-05T11:08:15Z
• dc.date.available 2021-05-05T11:08:15Z
• dc.date.issued 2019
• dc.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.
• dc.description.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.en
• dc.description.sponsorship This research has been supported by Kakao Corp., and partially funded by the European Unions Horizon 2020 research and innovation programme under grant agreement No 688382 (AudioCommons) and the Ministry of Economy and Competitiveness of the Spanish Government (Reference: TIN2015-69935-P).
• dc.format.mimetype application/pdf*
• dc.identifier.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.
• dc.identifier.uri http://hdl.handle.net/10230/47327
• dc.language.iso eng
• dc.publisher ACM Association for Computer Machinery
• dc.relation.ispartof 12th ACM International Conference on Web Search and Data Mining; 2019 Feb 11-15; Melbourne, Australia
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-69935-P
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/688382
• dc.rights © The Authors. Licensed under a Creative Commons License Attribution 4.0 International (CC BY 4.0)
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Data miningen
• dc.subject.keyword Machine learningen
• dc.subject.keyword Music recommender systemsen
• dc.subject.keyword Contentaware recommendationen
• dc.subject.keyword Challengesen
• dc.title Skip prediction using boosting trees based on acoustic features of tracks in sessionsen
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion