Breaking Down Netflix's Backend and Algorithms

Netflix started from online movie rentals company in 1997 and launched their first DVD rentals and sale site, in 1998. A year later, they debuted a subscription service which offers an unlimited DVD rentals for one monthly price. Through that, they have revolutionized and have become one of the world's leader in online streaming for movies and TV shows. With the algorithms that personalize each members' interests and a movie recommendation system that accurately predict choices, Netflix now has an overall members of 180+ million subscribers worldwide in the first quarter of 2020. 

So let's break down Netflix's backend and algorithms, to see how they predict our movies and TV shows choices and what system do they actually use to sort this out.

1. Machine learning algorithms 

Netflix uses 'Micro genre' that is made specifically, for instance; ‘Emotional Mother-Daughter Dramas based on Books’ or ‘Visually-striking Cult Horror Movies from the 1980s’. There are 27,000 types of specify genre, which based on where the member lives in as well in order to group the perfect content for each members' interests. Todd Yellin, a VP of Product Innovation of Netflix has defined Netflix's recommendation system as '3 legged chair' in which consists of 1. Netflix subscribers 2. Taggers 3. Machine learning algorithms. These 3 things are the key to find the right content to fit the right people and eventually lead them to spend more time watching Netflix. 

So what does Netflix sees when they are looking at each members' profile? They see what we are currently watching, what we have seen before and what we will see next, also what we have seen in the past year and what time that we usually use Netflix.

2. Categorized 

Each viewer will be categorized based on their interests. With this kind of system, it will surely have effect on each member recommendation page. The content will appear on top of the page which attracts most of our attention, then the next line will show different genre of movies and TV shows. For people who have similar interests, the system will combine contents together and show it on our page as well.

3. Break the taste 

Another method Netflix uses to keep their recommendation system fresh and new is the integration of 'Unexpected content'. By unexpected, it means that the content shown on our page is sometimes included the show that we have never heard of before or the ones that we have never laid our eyes on. The system will connect the point in each content, not just separated by genre, it will spot the similar story or message that appears closest to what we see. This is why our page keeps showing new or unique shows we never thought of clicking. It is truly the 'Break the taste' method. 

4. Implicit & explicit 

We can group Netflix content into 2 which are; implicit and explicit. Explicit data is a clear information that we as viewers do. For instance, thumbs up for series so that they know what kind content we actually into. Implicit data is when viewers spend huge amount of time binge watching the show. Implicit data is considered crucial to Netflix, our viewing history will be stored and integrated with other contents on Netflix. The contents will be tagged by Netflix employees, which they will have to watch every content in order to group and tag each content then separated them as location, feelings and actors.

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