Algorithms and taste-making: Exposing the Netflix Recommender System's operational logics

As the Streaming Wars continue to heat up, recommendation systems like the Netflix Recommender System (NRS) will become key competitive features for every major over-the-top video streamer. As a result, film and television production and consumption will increasingly be in the hands of semi-autonomous algorithmic technologies. But how do recommendation systems like the NRS work? What purposes do they serve? And what sorts of impacts are they having on film and television culture? To respond to these questions, this article will (1) examine how algorithms are impacting processes of taste-making and (2) re-evaluate some of the critical theoretical perspectives that have come to dominate the discourse surrounding algorithmic cultures. To do so, I join Bucher ((2016) Neither black nor box: Ways of knowing algorithms. In: S Kubitscko and A Kaun (eds) Innovative Methods in Media and Communication Research. Cham: Springer International Publishing, pp. 81–98; (2018) If…then: Algorithmic Power and Politics. London: Oxford University Press) in adopting a relational materialist perspective of algorithms and proceed to reverse engineer the NRS; an experiment that exposes the system’s circular and economic logics while highlighting the complex and networked nature of taste-making in the film and television industry.