Uses of AI in Netflix
So, how the AI is used in such companies to generate business . Here is how :-
Netflix not only has the largest worldwide subscriber base of any business but managed to keep growing it by +25% last year. Its market capitalization competes head to head with Disney, the most-valued entertainment company in the world. Netflix success story can not be explained without understanding their granular knowledge of their subscriber base and their AI driven focus on personalization.
Netflix not only looks at millions of ratings, searches and “plays” a day, but the entire viewing history of billions of hours of content streamed per month.
It took them 6 years to collect enough viewer data to engineer a show that became an worldwide success.
Since then, Netflix has increasingly used this formula for content creation achieving success rates of 82% compared to 35%-45% success rates of traditional TV shows.
Back in 2013 Netflix claimed
“There are 33 million different versions of Netflix”.
At that time, the company had 33 million subscribers.
Most internet companies use batch processing for personalization use cases such as recommendations, but Netflix realized that this was not quick enough for time sensitive scenarios such as new title launch campaigns or strong trending popularity cases. They moved to a near real-time (NRT) recommendation process to accelereate the learning process and roll out of test results.
Netflix aims to provide the artwork for each show that highlights the specific visual clue that is relevant for each individual member.
For each new title different images are randomly assigned to different subscribers, using the taste communities as an initial guideline.
(The example above is a of contextual image selection based on the type of profile. The contextual bandit selects the image of Robin Williams, a famous comedian, for comedy-inclined profiles while selecting an image of a kissing couple for profiles more inclined towards romance)
This translates into hundred of millions of personalized images continously being tested among its subscriber base.
Such As:
For the creation of the artwork, machine learning also plays a critical role thanks to a computer vision algorithm that scans the shows and picks the best images that will be tested among the taste communities.
Like this :-
Thats it , Pretty interesting huh ?
Thanks for Reading.
