Today life without Netflix feels like life without food!!!
Well as Netflix has become an important part of our lives, have you ever considered how come it suggests us just the right series/movie according to our choices?
I hope you didn’t because that’s what makes it special. It is an application which has surrounded us and we don’t even know how!
Though questions like these would have come in your mind –
How does Netflix come up with so much of our selected or favorite genres for its entire 100 million-plus subscriber group?
How does Netflix work?
Well, today you will get all the answers to it.
Everything is done using machine learning, AI, and the creativity behind what will make a subscriber choose a particular show to watch.
Machine learning and data science are helping Netflix personalize the experience for you all based on your history of picking shows to watch.
How does it work?
It works this by using some of the factors :
- how subscribers interact with their service (like their history, searches, queries and personal ratings )
- data collected from other subscribers on the site sharing similar interests like the subscribers
- connecting all that to information about the titles, such as their genre, categories, actors, release year, etc
It all begins when you first make a new profile and account for it, you are asked to choose a few genres that you like.
If you avoid this step, the algorithm takes a little longer to know and act according to your interests.
It takes these tags and the user behavioral data and then it uses idealistic machine learning algorithms that figure out what’s most important.
There are multiple working processes for creating a recommendation system. The method Netflix chooses simply depends on the size of the user base, the size of the catalog, and the basic goals.
- A basic implementation of a recommendation engine would be the EDITORIAL METHOD. In the editorial method, the application would make recommendations based on relatively fewer individuals.
- Another method can be SIMPLE COLLECTION METHOD where the platform makes suggestions based on the top products across the platform.
But you know what; Netflix does not use any of these methods because they don’t allow personalization, or cover the topics such as movie catalogs or user preferences.
- Instead, Netflix uses the PERSONALIZED METHOD where movies are suggested to the subscribers who are most likely to enjoy them based on a metric like a genre. Machine learning is necessary for this method because it uses user data to make informed suggestions. This way Netflix looks for the diversity in its audiences and its very large user base.
The process behind it …
- Because Machine learning uses probability to discover the preferences of a user liking a product, it is used to create innovative and smart platforms.
- To understand the probability of recommendation systems, we need to look at an example- let us say there is a UTILITY MATRIX, a probability model that places a score on the relationship between a subscriber and a movie type in order to predict their preferences.
- Here, we have three Netflix subscribers: Ram, Shyam, and Sita.
- Each has watched a few movies on Netflix and rated them and each has movies in the catalog they haven’t watched or rated yet.
- The user ratings are UTILITY SCORES which represent the relationship between the movie and the user.
- The utility scores are represented by the tick mark and X symbols.
- The tick marks represent movies that they’ve seen and liked and the Xs represent movies they’ve seen and not liked.
- The empty boxes represent movies that they haven’t seen yet.
- Netflix may suggest Ram watch a suspense movie since he enjoyed the thrilling plot of a horror movie which he watched last week.
- The system may recommend light-hearted films to Shyam, like a comedy romance because he didn’t enjoy the horror film.
- And because Sita enjoyed both horror movies and a romance movie, Netflix will suggest her movies like romantic thrillers.
- Hence Netflix recommends each user the shows personalized for them!
Some Algorithms used for Netflix Recommendation System are:
Trending Now Ranker — This algorithm captures all the temporary trends which Netflix collects as strong predictors. These short-term trends can range from a few minutes to a few days.
Top-N Video Ranker — This algorithm is similar to PVRs but it only looks at the head of the rankings and looks at the entire user catalog. It is optimized using various matrices.
Continue Watching Ranker — This algorithm looks at items that the viewer has consumed but has not completed. It calculates the probability of the user continue watching and includes other context-aware signals too like the point of abandoning etc.
Personalized Video Ranking (PVR) — This algorithm is used for general-purpose which usually filters the catalog by certain criteria (e.g. Indian TV shows, Romance, etc), combined with other features like popularity.
To Conclude –
With the help of machine learning and data science algorithms, Netflix has managed to build an amazing recommender system to keep its viewers hooked on their screens. Due to its smart recommendation engine, Netflix confessed that they save about $1 billion every year. 80% of the user stream is achieved because of the précised and smart recommendation system of Netflix.