2026-04-17T17:05:40Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/489012025-12-19T22:58:09Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Porcaro, Lorenzo author Castillo, Carlos author Gómez Gutiérrez, Emilia, 1975- author 2021-11-04T07:52:14Z 2021-11-04T07:52:14Z 2021 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. 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. Music Recommender Systems Diversity Information Technology Diversity by design in music recommender systems