Automatic playlist continuation using a hybrid recommender system combining features from text and audio

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• dc.contributor.author Ferraro, Andrés
• dc.contributor.author Bogdanov, Dmitry
• dc.contributor.author Yoon, Jisang
• dc.contributor.author Kim, KwangSeob
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2019-04-17T07:18:34Z
• dc.date.available 2019-04-17T07:18:34Z
• dc.date.issued 2018
• dc.description Comunicació presentada a: RecSys Challenge 2018, celebrat el 7 d'octubre de 2018, a Vancouver, Canadà.
• dc.description.abstract The ACM RecSys Challenge 2018 focuses on music recommendation in the context of automatic playlist continuation. In this paper, we describe our approach to the problem and the final hybrid system that was submitted to the challenge by our team Cocoplaya. This system consists in combining the recommendations produced by two different models using ranking fusion. The first model is based on Matrix Factorization and it incorporates information from tracks’ audio and playlist titles. The second model generates recommendations based on typical track co-occurrences considering their proximity in the playlists. The proposed approach is efficient and achieves a good overall performance, with our model ranked 4th on the creative track of the challenge leaderboard.
• 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, Yoon J, Kim K, Serra X. Automatic playlist continuation using a hybrid recommender system combining features from text and audio. In: RecSys Challenge '18. Proceedings of the ACM Recommender Systems Challenge 2018; 2018 02 Oct; Vancouver, Canada. New York: ACM; 2018. Art. 2. DOI: 10.1145/3267471.3267473
• dc.identifier.doi http://dx.doi.org/10.1145/3267471.3267473
• dc.identifier.uri http://hdl.handle.net/10230/37116
• dc.language.iso eng
• dc.publisher ACM Association for Computer Machinery
• dc.relation.ispartof RecSys Challenge '18. Proceedings of the ACM Recommender Systems Challenge 2018; 2018 02 Oct; Vancouver, Canada. New York: ACM; 2018. Art. 2.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/688382
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-69935-P
• dc.rights © 2018 Association for Computing Machinery
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Music recommender systems
• dc.subject.keyword Automatic playlist continuation
• dc.subject.keyword Collaborative filtering
• dc.subject.keyword Content-aware recommendation
• dc.subject.keyword Challenges
• dc.title Automatic playlist continuation using a hybrid recommender system combining features from text and audio
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion