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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.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.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.
dc.format.mimetype application/pdf
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.uri https://creativecommons.org/licenses/by/4.0/
dc.title Maximizing the engagement: exploring new signals of implicit feedback in music recommendations
dc.type info:eu-repo/semantics/conferenceObject
dc.subject.keyword Recommender systems
dc.subject.keyword Multi-stakeholder recommendation
dc.subject.keyword Implicit feedback
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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