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dc.contributor.author Porcaro, Lorenzo
dc.contributor.author Castillo, Carlos
dc.contributor.author Gómez Gutiérrez, Emilia, 1975-
dc.date.accessioned 2021-11-04T07:52:14Z
dc.date.available 2021-11-04T07:52:14Z
dc.date.issued 2021
dc.identifier.citation Porcaro L, Castillo C, Gómez E. Diversity by design in music recommender systems. Transactions of the International Society for Music Information Retrieval. 2021; 4(1):114–26. DOI: 10.5334/tismir.106/
dc.identifier.issn 2514-3298
dc.identifier.uri http://hdl.handle.net/10230/48901
dc.description.abstract Music Recommender Systems (Music RS) are nowadays pivotal in shaping the listening experience of people all around the world. Partly driven by the commercial application of this technology, music recommendation research has gained increasing attention both within and outside the Music Information Retrieval (MIR) community. Thanks also to the widespread use of recommender systems in music streaming services, it has been possible to enhance several characteristics of such systems in terms of performance, design, and user experience. Nonetheless, imagining Music RS only from an application-driven perspective may generate an incomplete view of how this technology is affecting people’s habitus, from the decision-making processes to the formation of musical taste and opinions. In this overview, we address the concept of diversity in music recommendation, and taking a value-driven approach we review diversity-related methodologies proposed in the Music RS literature. Additionally, by taking as an example the wider context of Information Technology (IT), we present the elements interacting in the diversity by design paradigm. We do that to acknowledge the lack of a comprehensive framework in Music RS research to address diversity, until now mostly driven by empirical results and fragmented in different application areas. Maintaining an interdisciplinary perspective, we discuss some challenges that MIR practitioners may face when researching Music RS, going beyond the search for better performance and instead questioning the theoretical foundations on which to base future research.
dc.description.sponsorship This work is partially supported by the European Commission under the TROMPA project (H2020 – grant agreement No. 770376). This work is also partially supported by the HUMAINT programme (Human Behaviour and Machine Intelligence), Joint Research Centre, European Commission. The project leading to these results received funding from “la Caixa” Foundation (ID 100010434), under the agreement LCF/PR/PR16/51110009.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Ubiquity Press
dc.relation.ispartof Transactions of the International Society for Music Information Retrieval. 2021; 4(1).
dc.rights © 2021 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/
dc.rights.uri http://creativecommons.org/licenses/by/4.0
dc.title Diversity by design in music recommender systems
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.5334/tismir.106
dc.subject.keyword Music Recommender Systems
dc.subject.keyword Diversity
dc.subject.keyword Information Technology
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/770376
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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