Original article was published on Deep Learning on Medium
Netflix: the world’s most popular subscription-based video streaming offered worldwide!
“Black Mirror, Dead to Me, and Medici.” — Top recommendations on Netflix to watch next.
But hey, how could Netflix possibly know which genre best fits the tastes of the user?
All thanks to the state-of-the-art Recommendation Engines.
A recommendation engine is a data filtering tool that uses data and algorithms to filter a catalog predicting relevant items and products to the user. Recommendation engines combine both content-based and collaborative-based approaches. Now the content-based approach relies on the intrinsic items the user has earlier showed interest in, thus suggesting a similar pattern of these properties. On the other hand, a collaborative-based approach analyzes all the users of the service and recommends a new user based on the relevant items of the other users having a similar taste.
1-Million-Dollar: the prize Netflix won for the ideas they project on better recommendation engines.
The million-dollar enterprise put in a lot of interest in idealizing ideas from deep learning and machine learning into the engineering behind the product.
Here’s what Netflix’s Senior Data Scientist, Mohammad Sabah said in 2014,
“75 percent of users select movies based on the company’s recommendations, and Netflix wants to make that number even higher.”
Today, Netflix is undoubtedly the epitome of content recommendation and personalized user experience.
How did a small DVD rental company grow to become a million-dollar enterprise?
Long story short.
The minds behind Netflix, Randolph, and Hastings gather the data of user interaction in the attempt of gaining better and clearer insights about how customers are using the DVD rental software. Upon which, they started gathering data through surveys and even making calls to the users’ residence. Lucky for them, as technology evolved so did their method of gathering data.
Even after two decades, Netflix is still on the verge of making improvements using key software-usage-insights. Eventually, AI and machine learning have a larger role to play, thus, allowing each subscriber to undergo experience according to the granular subscriber base knowledge.
What changes did Netflix undergo?
Most of the recommendation systems work with the help of having users ranking products based on a scale of 1 to 5. Well, Netflix got rid of it. The million-dollar company decided to move into a simpler version of thumbs up or thumbs down rating system along with the percentage demonstrating the compatibility between the user and the movie. The change took place for certain reasons as such that people generally rate movies on your behalf leaving their ratings to be biased. Precisely, up to a certain extent, this did have an impact on the algorithms (the input and the output). So instead of receiving the inputs in the form of 0 and 5, it now receives a more detailed binary “yes” and a binary “no” depending on whether the user appreciates the movie or not. And as for the output, the system must provide a percentage match between the user and the movie. You can imagine the amount of work the algorithm engineers and AI engineers had to put in.
Besides this, Netflix has decided to use more data to offer the best recommendation. Currently, most of the recommendations Netflix makes are based on their global audiences. Thus, the suggestions aren’t restricted to region-specific anymore. Another major reason why Netflix had rapid expansion.
Most used-cases of AI at Netflix
Thumbnails projection has made it even more simpler for users to choose the movies they prefer. Most of the users tend to choose movies or series based on the thumbnail to determine whether it is worth watching the movie or not. With time Netflix realizes that the title alone cannot convince the user to watch the movie, thus, their projection toward dynamic personalized thumbnail.
Every thumbnail chosen is algorithm-based, through which the users’ preference is chosen, and based on the past viewing history, the thumbnail selected has the highest rate of converting. For every program in Netflix, there is a diverse range of posters each of which caters to a specific group of viewers. As the algorithm gathers data and information on the user based on the thumbnails, it gives a better response in identifying the users’ genre.
👉Optimal streaming quality
The streaming quality is a crucial metric that specifically contributes to bounce rates. With over 140 million subscribers worldwide, it gets challenging for Netflix to offer the best streaming quality to its viewers. However, with the help of AI and machine learning, Netflix can now predict the future demands and position assets at strategic server locations way ahead of time. By pre-positioning the video assets closer to the subscribers, viewers can stream high-quality video even during peak hours without any interruption.
👉Tailored movie recommendations made just for you
Despite having two individuals log-in Netflix at the same time, both would be offered different program recommendations. Though this might seem obvious on the surface, however, the inside story is entirely different. Netflix’s recommendation system works on algorithm-based, but the major factor that increases the relevancy of these recommendations is because of machine learning and AI. The algorithm learns as data gets collected. Therefore, the more time you spend on Netflix, the more relevant programs will be recommended.
Netflix’s recommended engine worth over $1 Billion per year comes with a personalized grid of suggestions that is catered only to the viewers’ taste.