Have you ever wondered how streaming platforms like Netflix or Amazon Prime are always ready with an array of recommendations that seem tailor made for your tastes? Well you’re not the only one and as is it turns out, there’s a whole lot of complex data, smart algorithms and machine learning that go behind your next binge-watch.
Recommendation engines are powerful tools to enhance the customer experience by using machine learning and AI. They help users discover content that they’d enjoy but wouldn’t find themselves in the vast pool of choices. It’s a win-win for both parties because customers get to watch shows and movies of their liking without wasting time and the streaming services can boost revenue by increasing the time spent by users on their platform. So how exactly does this all work?
Basically, Netflix uses information regarding how you watch your shows, the ratings you give, what you search on the platform as well as how you use it to analyse your patterns and recommend you content to watch.
It all starts when you make an account on the website and Netflix asks you to select a few titles that you’d like, then it tracks your interaction within the website followed by data collection of other users who have similar interests as you and connecting all that user information to provide you with a highly sophisticated and personalized recommendation list. It has an in-house AI technology as well as outsourced machine learning to provide you the best and most suitable content when you want.
While Amazon makes use of an AI technology called Deep Learning that understands human psyche and provides results accordingly, this feature is useful for their on-demand video service as well because they can have a unique approach in recommending content to users that can skyrocket viewership tremendously and make users come back for more.
We’re hiring!
We are hiring two full-time junior to mid-level writers with the option to work remotely. You need to work a 5-hour shift and be available to write. Interested candidates should email their sample articles to [email protected]. Applications without a sample article will not be considered.
