skip to main content
10.1145/3609987.3610011acmotherconferencesArticle/Chapter ViewAbstractPublication PageschigreeceConference Proceedingsconference-collections
short-paper

Looking at the FAccTs: Exploring Music Industry Professionals' Perspectives on Music Streaming Services and Recommendations

Published:27 September 2023Publication History

ABSTRACT

Music recommender systems, commonly integrated into streaming services, help listeners find music. Previous research on such systems has focused on providing the best possible recommendations for these services’ consumers, as well as on fairness for artists who release their music on streaming services. While those insights are imperative, another group of stakeholders has been omitted so far: the many other professionals working in the music industry. They, too, are (in)directly affected by music streaming services. Therefore, this work explores the perspective of music industry professionals. We present a study that addresses the role of streaming services and recommender systems in their jobs. Results indicate this role is significant. Furthermore, participants feel that music recommender systems lack transparency and are insufficiently controllable, for both customers and artists. Finally, participants desire that music streaming services take charge of increasing recommendation diversity, and variety in consumers’ listening behavior and taste.

References

  1. Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato. 2020. Multistakeholder recommendation: Survey and research directions. User Modeling and User-Adapted Interaction 30, 1 (2020), 127–158. https://doi.org/10.1007/s11257-019-09256-1Google ScholarGoogle ScholarCross RefCross Ref
  2. Ivana Andjelkovic, Denis Parra, and John O’Donovan. 2016. Moodplay: Interactive Mood-Based Music Discovery and Recommendation. In Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization (Halifax, Nova Scotia, Canada) (UMAP ’16). ACM, New York, NY, USA, 275–279. https://doi.org/10.1145/2930238.2930280Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Robin Burke. 2002. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12, 4 (2002), 331–370. https://doi.org/10.1023/A:1021240730564Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Robin Burke. 2017. Multisided Fairness for Recommendation. In Proceedings of the Workshop on Fairness, Accountability and Transparency in Machine Learning, held at KDD 2017 (Halifax, Nova Scotia, Canada) (FAT/ML ’17). ACM, New York, NY, USA, 1–5. https://doi.org/10.48550/arXiv.1707.00093Google ScholarGoogle ScholarCross RefCross Ref
  5. Oscar Celma and Paul Lamere. 2011. If You Like Radiohead, You Might Like This Article. AI Magazine 32, 3 (Oct. 2011), 57–66. https://doi.org/10.1609/aimag.v32i3.2363Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Theodor Cimpeanu, Alessandro Di Stefano, Cedric Perret, and The Anh Han. 2023. Social diversity reduces the complexity and cost of fostering fairness. Chaos, Solitons & Fractals 167, Article 113051 (2023), 9 pages. https://doi.org/10.1016/j.chaos.2022.113051Google ScholarGoogle ScholarCross RefCross Ref
  7. Henriette Cramer, Jean Garcia-Gathright, Aaron Springer, and Sravana Reddy. 2018. Assessing and Addressing Algorithmic Bias in Practice. Interactions 25, 6 (oct 2018), 58–63. https://doi.org/10.1145/3278156Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Alvise De Biasio, Andrea Montagna, Fabio Aiolli, and Nicolò Navarin. 2023. A systematic review of value-aware recommender systems. Expert Systems with Applications 226 (2023), 16. https://doi.org/10.1016/j.eswa.2023.120131Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, and Ben Carterette. 2020. Evaluating Stochastic Rankings with Expected Exposure. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (Virtual Event, Ireland) (CIKM ’20). ACM, New York, NY, USA, 275–284. https://doi.org/10.1145/3340531.3411962Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Karlijn Dinnissen and Christine Bauer. 2023. Amplifying Artists’ Voices: Item Provider Perspectives on Influence and Fairness of Music Streaming Platforms. In Proceedings of the 31st Conference on User Modeling, Adaptation and Personalization (Limassol, Cyprus) (UMAP ’23). ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3565472.3592960Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Karlijn Dinnissen and Christine Bauer. 2023. Questionnaire: Music Industry Professionals’ View on Music Streaming Services and Recommender Systems. https://doi.org/10.5281/zenodo.8121152Google ScholarGoogle ScholarCross RefCross Ref
  12. Karlijn Dinnissen, Isabella Saccardi, Marloes Vredenborg, and Christine Bauer. 2023. Dataset: Music Industry Professionals’ Perspectives on Music Streaming Services and Recommendation. https://doi.org/10.5281/zenodo.8185736Google ScholarGoogle ScholarCross RefCross Ref
  13. Michael D Ekstrand, Anubrata Das, Robin Burke, and Fernando Diaz. 2022. Fairness in Information Access Systems. Foundations and Trends® in Information Retrieval 16, 1–2 (2022), 177 pages. https://doi.org/10.1561/1500000079Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Andrés Ferraro, Xavier Serra, and Christine Bauer. 2021. What Is Fair? Exploring the Artists’ Perspective on the Fairness of Music Streaming Platforms. In Human-Computer Interaction – INTERACT 2021: 18th IFIP TC 13 International Conference (Bari, Italy) (INTERACT ’21, Vol. 12933), C. Ardito, R. Lanzilotti, A. Malizia, H. Petrie, A. Piccinno, G. Desolda, and K. Inkpen (Eds.). Springer, Cham, Germany, 562–584. https://doi.org/10.1007/978-3-030-85616-8_33Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Batya Friedman, Peter H. Kahn, Alan Borning, and Alina Huldtgren. 2013. Value Sensitive Design and Information Systems. Springer Netherlands, Dordrecht, 55–95. https://doi.org/10.1007/978-94-007-7844-3_4Google ScholarGoogle ScholarCross RefCross Ref
  16. Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl. 2004. Evaluating collaborative filtering recommender systems. ACM Transaction on Information Systems 22, 1 (Jan. 2004), 5–53. https://doi.org/10.1145/963770.963772Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Dietmar Jannach and Christine Bauer. 2020. Escaping the McNamara Fallacy: Towards more Impactful Recommender Systems Research. AI Magazine 41, 4 (Dec 2020), 79–95. https://doi.org/10.1609/aimag.v41i4.5312Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Olena Khlystova, Yelena Kalyuzhnova, and Maksim Belitski. 2022. The impact of the COVID-19 pandemic on the creative industries: A literature review and future research agenda. Journal of Business Research 139 (feb 2022), 1192–1210. https://doi.org/10.1016/j.jbusres.2021.09.062Google ScholarGoogle ScholarCross RefCross Ref
  19. Sungchan Kim and Soyoung Park. 2017. Diversity Management and Fairness in Public Organizations. Public Organization Review 17, 2 (2017), 179–193. https://doi.org/10.1007/s11115-015-0334-yGoogle ScholarGoogle ScholarCross RefCross Ref
  20. Yu Liang and Martijn C. Willemsen. 2021. The Role of Preference Consistency, Defaults and Musical Expertise in Users’ Exploration Behavior in a Genre Exploration Recommender. In Proceedings of the 15th ACM Conference on Recommender Systems (Amsterdam, The Netherlands) (RecSys ’21). ACM, New York, NY, USA, 230–240. https://doi.org/10.1145/3460231.3474253Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Yu Liang and Martijn C. Willemsen. 2022. Exploring the Longitudinal Effects of Nudging on Users’ Music Genre Exploration Behavior and Listening Preferences. In Proceedings of the 16th ACM Conference on Recommender Systems (Seattle, WA, USA) (RecSys ’22). ACM, New York, NY, USA, 3–13. https://doi.org/10.1145/3523227.3546772Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Cataldo Musto, Marco de Gemmis, Pasquale Lops, Fedelucio Narducci, and Giovanni Semeraro. 2022. Semantics and Content-Based Recommendations. In Recommender Systems Handbook (3rd ed.), Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). Springer US, New York, NY, USA, 251–298. https://doi.org/10.1007/978-1-0716-2197-4_7Google ScholarGoogle ScholarCross RefCross Ref
  23. Ricardo S Oliveira, Caio Nóbrega, Leandro Balby Marinho, and Nazareno Andrade. 2017. A Multiobjective Music Recommendation Approach for Aspect-Based Diversification. In Proceedings of the 18th International Society for Music Information Retrieval Conference (Suzhou, China) (ISMIR ’17). 414–420. https://doi.org/10.5281/zenodo.1416999Google ScholarGoogle ScholarCross RefCross Ref
  24. Savvas Petridis, Nediyana Daskalova, Sarah Mennicken, Samuel F Way, Paul Lamere, and Jennifer Thom. 2022. TastePaths: Enabling Deeper Exploration and Understanding of Personal Preferences in Recommender Systems. In 27th International Conference on Intelligent User Interfaces (Helsinki, Finland) (IUI ’22). ACM, New York, NY, USA, 120–133. https://doi.org/10.1145/3490099.3511156Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Evaggelia Pitoura. 2020. Social-Minded Measures of Data Quality: Fairness, Diversity, and Lack of Bias. ACM Journal of Data and Information Quality 12, 3, Article 12 (jul 2020), 8 pages. https://doi.org/10.1145/3404193Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Lorenzo Porcaro, Emilia Gómez, and Carlos Castillo. 2022. Diversity in the Music Listening Experience: Insights from Focus Group Interviews. In ACM SIGIR Conference on Human Information Interaction and Retrieval (Regensburg, Germany) (CHIIR ’22). ACM, New York, NY, USA, 272–276. https://doi.org/10.1145/3498366.3505778Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Francesco Ricci, Lior Rokach, and Bracha Shapira. 2022. Recommender Systems: Techniques, Applications, and Challenges. In Recommender Systems Handbook (3rd ed.). Springer US, New York, NY, 1–35. https://doi.org/10.1007/978-1-0716-2197-4_1Google ScholarGoogle ScholarCross RefCross Ref
  28. Kyle Robinson, Dan Brown, and Markus Schedl. 2020. User Insights on Diversity in Music Recommendation Lists. In Proceedings of the 21st International Society for Music Information Retrieval Conference (Montreal, Canada, October 11–16) (ISMIR ’20). ISMIR, 446–453. https://doi.org/10.5281/zenodo.4245464Google ScholarGoogle ScholarCross RefCross Ref
  29. Markus Schedl, Peter Knees, Brian McFee, and Dmitry Bogdanov. 2022. Music Recommendation Systems: Techniques, Use Cases, and Challenges. In Recommender Systems Handbook (3rd ed.), Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). Springer US, New York, NY, USA, 927–971. https://doi.org/10.1007/978-1-0716-2197-4_24Google ScholarGoogle ScholarCross RefCross Ref
  30. Ignacio Siles, Amy Ross Arguedas, Mónica Sancho, and Ricardo Solís-Quesada. 2022. Playing Spotify’s game: artists’ approaches to playlisting in Latin America. Journal of Cultural Economy 15, 5 (2022), 17 pages. https://doi.org/10.1080/17530350.2022.2058061Google ScholarGoogle ScholarCross RefCross Ref
  31. Rashmi Sinha and Kirsten Swearingen. 2002. The Role of Transparency in Recommender Systems. In CHI ’02 Extended Abstracts on Human Factors in Computing Systems (Minneapolis, MA, USA) (CHI EA ’02). ACM, New York, NY, USA, 830–831. https://doi.org/10.1145/506443.506619Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Nasim Sonboli, Jessie J. Smith, Florencia Cabral Berenfus, Robin Burke, and Casey Fiesler. 2021. Fairness and Transparency in Recommendation: The Users’ Perspective. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (Utrecht, The Netherlands) (UMAP ’21). ACM, New York, NY, USA, 274–279. https://doi.org/10.1145/3450613.3456835Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Jonathan Stray, Alon Y. Halevy, Parisa Assar, Dylan Hadfield-Menell, Craig Boutilier, Amar Ashar, Lex Beattie, Michael D. Ekstrand, Claire Leibowicz, Connie Moon Sehat, Sara Johansen, Lianne Kerlin, David Vickrey, Spandana Singh, Sanne Vrijenhoek, Amy X. Zhang, McKane Andrus, Natali Helberger, Polina Proutskova, Tanushree Mitra, and Nina Vasan. 2022. Building Human Values into Recommender Systems: An Interdisciplinary Synthesis. CoRR abs/2207.10192 (2022), 64. https://doi.org/10.48550/arXiv.2207.10192 arXiv:2207.10192Google ScholarGoogle ScholarCross RefCross Ref
  34. Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, and Philip E et al. Bourne. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3, 1 (2016), 9 pages. https://doi.org/10.1038/sdata.2016.18Google ScholarGoogle ScholarCross RefCross Ref
  35. Eva Zangerle and Christine Bauer. 2022. Evaluating recommender systems: survey and framework. Comput. Surveys 55, 8, Article 170 (2022), 38 pages. https://doi.org/10.1145/3556536Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Looking at the FAccTs: Exploring Music Industry Professionals' Perspectives on Music Streaming Services and Recommendations

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        CHIGREECE '23: Proceedings of the 2nd International Conference of the ACM Greek SIGCHI Chapter
        September 2023
        218 pages
        ISBN:9798400708886
        DOI:10.1145/3609987

        Copyright © 2023 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 27 September 2023

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • short-paper
        • Research
        • Refereed limited
      • Article Metrics

        • Downloads (Last 12 months)68
        • Downloads (Last 6 weeks)10

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format