Maximizing the engagement: exploring new signals of implicit feedback in music recommendations

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• dc.contributor.author Ferraro, Andrés
• dc.contributor.author Oramas, Sergio
• dc.contributor.author Quadrana, Massimo
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2021-01-14T11:24:20Z
• dc.date.available 2021-01-14T11:24:20Z
• dc.date.issued 2020
• dc.description Comunicació presentada a: 14th ACM Conference on Recommender Systems (RecSys 2020) celebrat el 25 de setembre de 2020 de manera virtual.
• dc.description.abstract Music recommendation engines play a pivotal role in connecting artists with their listeners. Optimizing myopically only for user satisfaction may lead systems to recommend just a small fraction of all the available artists, or to recommend artists to users who might engage with them only in the short-term. In this work, we investigate such effects by exploring different signals of implicit feedback provided by users when using a music service (i.e., counting the number of tracks, days or times a user listens to an artist) and propose novel combined signals. Our approaches are evaluated over four different datasets, combining traditional user-centered evaluation metrics with artist-based ones, which allows us to measure the quality of the recommendations and the potential engagement with the recommended artists. Our experiments reveal that the selection of the implicit feedback signal has a significant impact on the quality of the recommendations. In addition, we show that the proposed signals increase the chances of a higher engagement between users and the artists they get recommended.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Ferraro A, Oramas S, Quadrana M, Serra X. Maximizing the engagement: exploring new signals of implicit feedback in music recommendations. In: Bogers T, Koolen M, Petersen C, Mobasher, Tuzhilin A, Sar Shalom O, Jannach D, Konstan JA, editors. Proceedings of the Workshops on Recommendation in Complex Scenarios and the Impact of Recommender Systems co-located with 14th ACM Conference on Recommender Systems (RecSys 2020);25 Sep 2020; Brazil. Aachen: CEUR Workshop Proceedings; 2020.
• dc.identifier.issn 1613-0073
• dc.identifier.uri http://hdl.handle.net/10230/46172
• dc.language.iso eng
• dc.publisher CEUR Workshop Proceedings
• dc.relation.ispartof Bogers T, Koolen M, Petersen C, Mobasher, Tuzhilin A, Sar Shalom O, Jannach D, Konstan JA, editors. Proceedings of the Workshops on Recommendation in Complex Scenarios and the Impact of Recommender Systems co-located with 14th ACM Conference on Recommender Systems (RecSys 2020);25 Sep 2020; Brazil. Aachen: CEUR Workshop Proceedings; 2020
• dc.rights Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/)
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Recommender systemsen
• dc.subject.keyword Multi-stakeholder recommendationen
• dc.subject.keyword Implicit feedbacken
• dc.title Maximizing the engagement: exploring new signals of implicit feedback in music recommendationsen
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion