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Eriksson, Maria et al. How does Spotify package music? Spotify teardown: inside the black box of streaming music. The MIT Press. 2019, p. 115-47.

Facebook. What are Facebook recommendations? 2020 & What are Instagram recommendations? 2020

Goldschmitt, KE & Seaver, Nick. Shaping the stream: techniques and troubles of algorithmic recommendation. In Cook, Nicholas et al. The Cambridge Companion to Music in Digital Culture. 2019, p. 63-81

GOV.UK. Research into the impact of streaming services’ algorithms on music consumption. Centre for data ethics and innovation, (Dept. for science, innovation and technology), Feb 2023

Hallinan, Blake & Striphas, Ted. Recommended for you: The Netflix Prize and the production of algorithmic culture. New media and society. 2016

Hardesty, Larry. Explained: neural networks. MIT, 2017

Helberger, Natali. “On the democratic role of news recommenders”. Digital Journalism: algorithms, automation & news, 7/8 (2019), p. 993-1012. Also in Thurman, Neil (ed.). Algorithms, Automation & News. Routledge, 2021

Holmes, Dawn E., Recommender systems. Big Data: a very short introduction. OUP VSI. 2017, p. 80-89 — Library resource

Jones, Elliot. Inform, educate, entertain…and recommend? Exploring the use and ethics of recommendation systems in public service media [BBC]. Ada Lovelace Institute, November 2022.

Kuosmanen, Ville. Improving the explainability of user-facing recommender systems. Thesis, St Andrews. 2020

Leibowicz, Claire & Stephan, Adriana. Local Newsrooms Should Adopt AI Ethics as They Adopt AI: 5 Recommendations

Lewis, Paul & McCormick, Erin. How an ex YouTube insider investigated its secret algorithm. Guardian. 2018

Maheshwari. Sapna. On YouTube Kids, startling videos slip past filters. NY Times, 2017

Marconi, Francesco, Daldrup, Rajiv & Tilland, Pant. Acing the algorithm beat. 2019. Nieman Lab. 2019

Milano, Silvia, Taddeo, Mariarosaria & Floridi, Luciano. Recommender systems and their ethical challenges. Oxford Internet Institute. 2019

Nadeem. Moin. How YouTube recommends videos. Medium. 2018

O’Callaghan, Derek et al. Down the (White) Rabbit Hole: The extreme right and online recommender systems. Social science computer review. 2015, 33/4, p. 459-78

O’Neill, Cathy. Weapons of math destruction. Penguin, 2020 — Library resource (online & hard copy)

Pariser, Eli. The filter bubble: what the internet is hiding from you. Penguin, 2011 — Library resource

Ricci Francesco et al. Recommender Systems: Introduction and Challenges. In Ricci Francesco et al. (eds.) Recommender systems handbook. Springer, 2015 — Library resource

Rieder, Bernhard. Introducing the YouTube data tools. 2015 & YouTube data tools (1.22)

Rogers, Richard. YouTube teardown. Doing Digital Methods. 2019, p. 249-59

Sanchez Bocanegra et al. HealthRecSys: A semantic content-based recommender system to complement health videos. BMC Medical Informatics and Decision Making. p. 17-63. 2017

Seaver, Nick. Captivating algorithms: Recommender systems as traps. Journal of Material Culture. 24/4, 2019. p. 421–36

Seaver, Nick. Computing taste. University of Chicago Press, 2022

Smith, Ben. How Tiktok reads your mind. NY Times, 2021

Stray, Jonathan, Adler, Steven & Hadfield-Menell, Dylan. What are you optimizing for? Aligning recommender systems with human values. 2020

Thomson, Clive. YouTube’s plot to silence conspiracy theories. Wired. 2020

Thurman, Neil et al. (eds.). Algorithms, Automation & News. Routledge, 2021. Library resource See also Digital Journalism, vol 7/8, 2019. Library resource