For those of you who are uninitiated, here is an oversimplification of how Netflix streams content to viewers all around the globe. For reading on how to get on Netflix Similar App Development, studying features and understanding business model, please go to the linked page.
Netflix has set up different Content Delivery Networks (CDN) all across the globe. These CDNs take the original data (entire website and media included) and copy it across hundreds of servers spread all over the world. This means, if someone from Sydney connects with Netflix, the CDN, instead of connecting to the main server based in the United States, it connects to the nearest server in Australia. This largely reduces the lag time- time is taken to send a request and receive a response. Again, this is an oversimplification of something very complex that goes into their video streaming app development.
Now, why did we discuss how Netflix streams content all over the world?
When we are talking about the entire ‘globe’, one thing has to be kept in mind that users all over the continents behave and interact differently, especially in terms of preferences. No two single users would watch similar content at the same time.
Chaitanya Ekanadham, Data science manager at Netflix says, “Providing a quality streaming experience for this global audience is an immense technical challenge. A large portion of this is engineering effort required to install and maintain servers throughout the world, as well as algorithms for streaming content from those servers to our subscribers’ devices.“
This is an astronomical task which cannot be fulfilled by humans alone. This requires close observation on user’s viewing patterns.
He further adds, “As we expand rapidly to audiences with diverse viewing behavior, operating on networks and devices with widely varying capabilities, a “one size fits all” solution for streaming video becomes increasingly suboptimal.”
Let’s get under the hood and check how, with the help of Machine Learning, Netflix enhances the user experience in their video streaming app development.
Netflix has millions of individual user profiles. Each profile viewing experience is vastly different. “What we see from those profiles is the following kinds of data – what people watch, what they watch after, what they watch before, what they watched a year ago, what they’ve watched recently and what time of day”. Says Todd Yellin, Netflix’s vice president of product innovation.
This data forms the first base for the machine to learn the user behavior.
Next, there is a staff of in-house and freelance viewers who watch every single minute of the shows and movies and feed the machine with relevant tags. These tags vary depending upon the show. Be it a movie set in space, or about the medieval times, these people from the staff tag the content accordingly.
“We take all of these tags and the user behavior data and then we use very sophisticated machine learning algorithms that figure out what’s most important. How much should it matter if a consumer watched something yesterday? Should that count twice as much or ten times as much compareed to what they watched a whole year ago? How about a month ago? How about if they watched ten minutes of content and abandoned it or they binged through it in two nights? How do we weight all that? That’s where machine learning comes in. What those three things create for us is ‘taste communities’ around the world. It’s about people who watch the same kind of things that you watch.” says Todd Yellin.
Netflix divides its user’s data input into 2 different categories- implicit and explicit. “Explicit data is what you literally tell us: you give a thumbs up to The Crown, we get it,” Yellin explains. “Implicit data is really behavioural data. You didn’t explicitly tell us ‘I liked Unbreakable Kimmy Schmidt’, you just binged on it and watched it in two nights, so we understand that behaviourally. The majority of useful data is implicit.”
This helps in presenting an explanation for the choice of rows using a member’s implicit genre preferences — recent plays, ratings, and other interactions — or explicit feedback provided through our taste preferences survey.