Netflix’s Recommendation System using Machine Learning By Aarohi Manchanda
How does Netflix know that you like Money Heist? And no, it’s not the FBI spying on your “Bella Ciao” wall-poster.
After a long day of work, all anyone wants to do is to watch the right show with their favorite food. It has become rather obvious that in today’s world, the rise of the OTT sector is paramount. For streaming aces like Netflix and Amazon Prime Video, the pandemic year 2020 has especially proven to be a game-changer.
Netflix, whose market capitalization is estimated to be over $217.08 billion, has transformed from this once-underdog company that started with posting DVDs to people’s homes into a global financial giant. With its 160 million members in over 190 countries, it has become the world’s leading internet television network.
The real question is, with over 15000 movies and shows to pick from, how does anyone really decide what to watch? I mean, come on! I’m sure I’m not the only one who waits till the perfect show is on before starting to eat, right? This is where their most successful algorithm, Netflix Recommendation Engine (NRE) comes into play.
How important is a Recommender System?
With the rapid growth of new technologies in the entertainment industry, it is easy to forget that entertainment is still an inherently emotional product. The key to engaging a large audience is the personalization of content and creating an emotional connection with the user. Everyone wants a smart streaming platform that anticipates their needs and recommends the most appropriate titles in the shortest amount of time. Hence, recommendation algorithms are at the core of the Netflix product.
Role of NRE in Netflix’s Development
- Around 80% of Netflix viewer activity is driven by personalized recommendations.
- The average Netflix user has watched 49 days’ worth of TV shows and movies since creating an account.
- Netflix’s recommendation algorithms generate over $1 Billion per year of Netflix revenue (as savings on customer acquisition).
- Netflix just has a 90-second window to help viewers find a movie or a TV show before they leave the platform and visit some other service.
- Netflix splits viewers up into more than two thousand taste groups. Which one you’re in dictates the recommendations you get.
These facts give enough evidence of why Netflix is adamant about constantly improving its recommendation system, and as a result, enhancing the experience of its users. But how? The simple answer to this is- By personalizing everything.
What is Netflix using as its recommender system?
Netflix invests extensively in Machine Learning, a subset of artificial intelligence, to continually improve their member experience and optimize their service end-to-end. They use it to help their algorithms “learn” from their users, without the need for human intervention. Machine learning gives the platform the ability to automate millions of decisions based on user activities.
The Netflix engineering team used the conjunction of Data Science and Machine Learning to build their Recommender Engine. Data Analysis is also an eminent factor for the working of these algorithms. For example, whenever you select a new movie to watch, Netflix’s recommendation system stores fresh data related to your preferences, likes and dislikes. It analyses this data and provides an updated algorithm to give more accurate recommendations than before.
How is Netflix Personalizing User Experience?
To help you find the right show or movie with minimal effort, Netflix’s Recommendation System takes into account a lot of factors, such as:
- Your interactions with the service (like viewing history and how you rated other titles).
- Other members with similar tastes and preferences.
- Information about the titles, such as their genre, categories, actors, release year, etc.
- The time of day you watch.
- The devices you are watching Netflix on.
- How long you watch.
All of these pieces of data are used as inputs that are processed in their algorithms.
The NRE system also ranks each title within the row, and then ranks the row themselves, using algorithms and complex systems to provide a personalized experience.
In each row there are three layers of personalization:
• The choice of row (e.g., Continue Watching, Trending Now, Award-Winning Comedies, etc.)
- Which titles appear in the row.
- The ranking of those titles.
Your Netflix homepage is intended to show personalized titles in the most appealing way possible.
Another intelligent strategy that distinguishes Netflix from other platforms is its personalized artwork. This has been an important component in introducing new and unknown titles to the audience.
Let’s take an example of the movie — La La Land.
Now if you’re someone who likes romantic movies and has a viewing history full of them, the artwork displayed to you will be a romantic poster.
However, if you are an Emma Stone fan and have seen her previous works, the artwork displayed to you will be of Emma Stone in that film.
Future Potential of Recommendation Systems
Over the last decade, various content, collaborative, and hybrid approaches have been presented, and multiple algorithms have been created for recommender systems. Despite these advancements, the current generation of recommender systems still has to be improved for recommendation methods to be more successful in a wider range of applications. Current recommendation techniques have some flaws and are less accurate, but we can use extensions to improve recommending capabilities.
Recommender systems may be a very effective tool in a company’s armoury, and future improvements will only add to its worth. Some of the uses include the ability to predict seasonal purchases based on suggestions, identify key transactions, and provide better recommendations to consumers, all of which may help boost customer retention and loyalty.
Netflix’s machine learning model is only the beginning of this field’s exploration, and given that Prime Video, Hotstar, and other OTT platforms have begun to use this recommender system as well, there is an immense potential for future development.