Abstract
Spotify is a world class platform for music streaming, and it offers various kinds of services. As with many digital platforms, Spotify uses artificial intelligence to personalize the user experience, also known as a recommender system. This study investigates what role Spotify’s recommender system plays in the use of Spotify and if there are any differences in satisfaction between different ages and gender. Therefore, we conducted a survey about how customers are using Spotify, which was shared in different forums. In total we received 159 answers with respondents from 21 different countries. One of the main findings was that the three services “Make your own playlist”, “Playlist made by Spotify” and “Recommended songs” are the most popular. Also, a correlation was made to investigate the relationship between the satisfaction of “Recommended songs” and if customers add them to their own playlists. The Spearman’s Rank Correlation Coefficient was 0.43 (significant at the 0.01 level), which is a moderate value. This means that almost half of the time the Spotify users place the recommended songs in their playlist. Further, two conclusions were arrived at. Firstly, the recommender system plays a major part in how customers use Spotify. Secondly, we cannot see that age and gender would significantly affect the satisfaction of the recommended songs that Spotify suggest.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Spotify. Spotify Reports Third Quarter 2021 Earnings – Spotify. https://newsroom.spotify.com/2021-10-27/spotify-reports-third-quarter-2021-earnings/. Accessed 28 Oct 2021
Barata, M.L., Coelho, P.S.: Music streaming services: understanding the drivers of customer purchase and intention to recommend. Heliyon 7(11) (2021). Article no. e07783
Lozic, J., Vojkovic, G., Milkovic, M.: “Financial” aspects of spotify streaming model. In: 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO), Opatija, Croatia, 28 September–2 October 2020
Aguiar, L.: Let the music play? Free streaming and its effects on digital music consumption. Inf. Econ. Policy 41, 1–14 (2017)
Hashemi, H., Pappu, A., Tian, M., Chandar, P., Lalmas, M., Carterette, B.: Neural instant search for music and podcast. In: Proceedings of the ACM SIGKDD International Conference on Knowledge and Data Mining, Virtual - Online, 14–18 August (2021)
Jones, R., et al.: Current Challenges and Future Directions in Podcast Information Access. SIGIR 2021 - Virtual Event, Canada, 11 – 15th of July (2021)
Chen, J., Li, K., Zhang, Z., Li, K., Yu, P.S.: A survey on applications of artificial intelligence in fighting against COVID-19. ACM Comput. Surv. 54(8) (2021). Article no. 158
Benavides, D.J., Arévalo-Cordero, P., González, L.G., Hernández-Callejo, L., Jurado, F., Aguado, J.A.: Method of monitoring and detection of failures in PV system based on machine learning. Revista Facultad de Ingeniería 102, 26–43 (2022)
Tofalvy, T., Koltai, J.: “Splendid isolation”: the reproduction of music industry inequalities in Spotify’s recommendation system. New Media Soc. 1–25 (2021)
Dwivedi, R.: What Are Recommendation Systems in Machine Learning? Analytic Steps, April 16 2021. https://www.analyticssteps.com/blogs/what-are-recommendation-systems-machine-learning. Accessed 28 Oct 2021
Lunardi, G.M., Machado, G.M., Maran, V., Oliveira, J.P.M.: A metric for Filter Bubble measurement in recommender algorithms considering the news domain. Appl. Soft Comput. 97(Part A) (2020). Article no. 106771
Melchiorre, A.B., Rekabsaz, N., Parada-Cabaleiro, E., Brandl, S., Lesota, O., Schedl, M.: Investigating gender fairness of recommendation algorithms in the music domain. Inf. Process. Manag. 58(5) (2021). Article no. 102666
Ekstrand, M.D., et al.: All the cool kids, how do they fit in? Popularity and demographic biases in recommender evaluation and effectiveness. In: Proceedings of Machine Learning Research, vol. 81, pp. 1–15. Conference on Fairness, Accountability and Transparency (2018)
Spotify. Company info – Spotify. https://newsroom.spotify.com/company-info/. Accessed 25 Oct 2021
Spotify. Premium Plans – Spotify. https://support.spotify.com/us/article/premium-plans. Accessed 25 Oct 2021
Whitehouse, K.: How Spotify Uses Artificial Intelligence, Big Data and Machine Learning. Data Science Central, March 7 (2021)
Afoudi, Y., Lazaar, M., Al Achhab, M.: Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network. Simul. Model. Pract. Theory 113 (2021). Article no. 102375
Spotify. App Help – Spotify. https://support.spotify.com/us/app_help/features/. Accessed 26 Oct 2021
Gomes, I., Pereira, I., Soares, I., Antunes, M., Au-Yong-Oliveira, M.: Keeping the beat on: a case study of spotify. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds.) WorldCIST 2021. AISC, vol. 1366, pp. 337–352. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72651-5_33
Bryman, A., Bell, E.: Business Research Methods, 4th edn. Oxford University Press, Oxford (2015)
Hodgson, T.: Spotify and the democratisation of music. Pop. Music 40(1), 1–17 (2021)
Tofalvy, T., Koltai, J.: “Splendid Isolation”: The reproduction of music industry inequalities in Spotify’s recommendation system. New Media Soc.,1–25 (2021)
Yürekli, A., Bilge, A., Kaleli, C.: Exploring playlist titles for cold-start music recommendation: an effectiveness analysis. J. Ambient Intell. Humaniz. Comput. 12(11), 10125–10144 (2021). https://doi.org/10.1007/s12652-020-02777-3
Saunders, M.N.K., Cooper, S.A.: Understanding Business Statistics: An Active-Learning Approach. DP Publications, London (1993)
Acknowledgements
The authors would like to thank the respondents to the anonymous survey for their time and availability.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix A – Spearman’s Rank Correlation Coefficient Calculation
Appendix A – Spearman’s Rank Correlation Coefficient Calculation
Variable X: “How often are you satisfied with the songs that Spotify recommends?”
Variable Y: “How often do you add a recommended song to your playlist?”
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Björklund, G., Bohlin, M., Olander, E., Jansson, J., Walter, C.E., Au-Yong-Oliveira, M. (2022). An Exploratory Study on the Spotify Recommender System. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F. (eds) Information Systems and Technologies. WorldCIST 2022. Lecture Notes in Networks and Systems, vol 469. Springer, Cham. https://doi.org/10.1007/978-3-031-04819-7_36
Download citation
DOI: https://doi.org/10.1007/978-3-031-04819-7_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-04818-0
Online ISBN: 978-3-031-04819-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)