Thumbnails, the small artworks used to visually sort the user interface on Video-on-demand platforms, are personalised and customised for users on prominent Subscription Video-On-Demand platform Netflix. This content strategy of customisation falls in line with other content strategies from Netflix’s past and is reflective of the increased personalisation of the consumer experience in the age of digital distribution. A pilot survey of Netflix user thumbnails gathers an initial set of data on the breadth of thumbnail personalisation on Netflix, and reflects on the ways that thumbnails, understood as paratexts, can demonstrate divergent content appeals to users, broadening the available ways to understand film and television texts and platforms themselves. This research centres the role of the thumbnail as a paratext that frames understanding of content and the broader platform, and in doing so visualises and humanises a vital aspect of algorithmic culture.
AlexanderN (2016) Catered to Your Future Self: Netflix’s “Predictive Personalisation” and the mathematization of Taste”. In: McDonaldKSmith-RowseyK (eds) The Netflix Effect: Technology and Entertainment in the 21stCentury. New York, NY: Bloomsbury Academic, pp. 81–98.
2.
AmatriainX (2013) Big & personal: data and models behind Netflix recommendations. In BigMine ’13: 2nd International Workshop on Big DataStreams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, Chicago, IL, August 11, 2013, pp. 1–6. Available at: https://dl.acm.org/citation.cfm?id=2501222.
3.
AndrejevicM (2013) Infoglut: How Too Much Information Is Changing the Way We Think and Know. New York, NY: Routledge.
4.
AndrejevicM (2020) Automated Media. New York, NY: Routledge.
5.
BeckJCDavenportTH (2001) The Attention Economy: Understanding the New Currency of Business. Cambridge, UK: Harvard University Press.
6.
BermanJ (2019) Games of Thrones May Be the Last True Water-Cooler Show. Is That Such a Bad Thing?Time.
GenetteG (1997) Paratexts: Thresholds of Interpretation. Trans. Jane E. Lewin. Cambridge, UK: Cambridge University Press.
11.
GrayJ (2010) Sold Separately: Promos, Spoilers, and Other Media Paratexts. New York, NY: New York University Press.
12.
GrayJ (2015) Afterword: Studying Media with and without Paratexts ” in Popular Media Cultures: Fans, Audiences and ParatextsLincoln Geraghty. London, UK: Palgrave Macmillan.
13.
GrayJ (2017) Text in Keywords for Media Studies Edited by Laurie Ouellette and Jonathan Gray. New York, NY: New York University Press.
14.
HallinanBStriphasT (2014) Recommended for you: the Netflix Prize and the production of algorithmic culture. New Media & Society18(1): 117–137.
15.
HesmondhalghDLobatoR (2019) Television device ecologies, prominence and datafication: the neglected importance of the set-top box. Media, Culture & Societyculture & Society41(7): 958–974.
16.
JenkinsH (2006) Convergence Culture: Where Old and New Media Collide. New York, NY: New York University Press.
17.
JennerM (2018) Netflix and the Re-invention of Television. London, UK: Palgrave Macmillan.
18.
JohnsonC (2017) Beyond catch-up. Critical Studies in Television: The International Journal of Television Studies12(2): 121–138.
19.
JohnsonC (2019) Online TV. New York, NY: Routledge.
20.
KellyJP (2018) Algorithmic Advertising and the Perils of Personalisation. Flow.
21.
KleckerC (2015) The other kind of film frames: a research report on paratexts in film. Word & Image31(4): 402–413. DOI: 10.1080/02666286.2015.1053035.
LiLChuWLangfordJ, et al (2010) A Contextual- bandit approach to personalised news article recommendation. In WWW’10: Proceedings of the 19th international conference on World wide web. Raleigh, NC. April 26-30 2010.
24.
LightBBurgessJDuguayS (2018) The walkthrough method: An approach to the study of apps. New Media & Society.
25.
LobatoR (2019) Netflix Nations: The Geography of Digital Distribution. New York, NY: New York University Press.
26.
LotzAD (2017) A Treatise on Internet-Distributed Television. Ann Arbor, MI: Michigan Publishing.
27.
LotzAD (2019) Show me the money: How revenue strategies change the creative possibilities of internet-distributed television. In: DeuzeMPrengerM (eds) Making Media: Production, Practices, and Professions. The Netherlands: Amsterdam University Press, pp. 337–346.
MorrisDZ (2016) Netflix says Geography, Age, and Gender Are ‘Garbage’ for Predicting Tastes. New York, NY: Fortune. Available at: http://fortune.com/2016/03/27/netflix-predicts-taste/ (accessed on 27 March 2016).
PajkovicN (2021) Algorithms and Taste-Making: Exposing the Netflix Recommender System's Operational Logics. Thousand Oaks, CA: Convergence.
32.
PerksL (2015) Media Marathoning, Immersions in Morality. Washington, DC: Lexington Books.
33.
QUT (2020) Netflix – A Zebra Among Horses: QUT Researcher. Brisbane, QLD: Queensland University of Technology, press release. Available at: https://www.qut.edu.au/news?id=168991 (accessed on 11 September 2020).
34.
RosenzweigR (2003) Scarcity or Abundance? Preserving the Past in a Digital Era. The American Historical Review108(3): 735–762.
35.
Smith-RowseyD (2016) Imaginative Indices and Deceptive Domains: How Netflix’s Categories and Genres Redefine the Long Tail. In: McDonaldKSmith-RowseyK (eds) The Netflix Effect: Technology and Entertainment in the 21st Century. New York, NY: Bloomsbury Academic, pp. 63–79.
