This video explains a topic in relation to Automated Decision Making and its involvement in the News and Media, it provides viewers with an understanding of Netflix’s recommendation system and how it is curated. A recommendation system is technically defined as a subclass of information filtering that provides suggestions for items that are most pertinent to a particular user, however, you may know it when you’re scrolling through Netflix and come across a genre called “top picks for you.” Recommendation algorithms are at the core of the Netflix product, Netflix says “they provide our members with personalized suggestions to reduce the amount of time and frustration to find some great content to watch.” The algorithms on which these recommendations are built are constructed from a consumer’s previous viewing history and what other people who enjoy this content genre also like watching. These algorithmic tools are used to identify content that is in the best interest of the user, they’re seen as a set of instructions and calculations designed to solve problems. And I guess the problem here it is wishing to solve is the headache of what to watch next. Netflix first introduced personalized movie recommendations in the year 2000 with the algorithm originally being called Cinematch. 

The process of constructing this video has been weeks in the making, it involved a lot of research and understanding technical terms that I had never come across. However, the journey was worth the outcome and I am pleased with my final copy of the assignment. I learnt a lot about Netflix and the hidden ways it entices the audience to keep them engaged for as long as possible but I also expanded my knowledge on ADM itself.