Netflix’s Algorithm: How Does Netflix Use AI to Personalize Recommendations?

Whenever you open Netflix, you immediately find a show or movie that feels like it was chosen just for you. This isn’t a coincidence. With over 282 million subscribers across 190 countries, Netflix has revolutionized the way we watch TV and movies. The reason for its success? A smart recommendation system is driven by artificial intelligence that makes sure you always get content you’re likely to enjoy.

In fact, about 75% of what people watch on Netflix comes from its personalized recommendations. This means that for the majority of users, the algorithm guides them toward their next favorite show or movie. Whether you love heart-pounding thrillers, light comedies, or obscure indie films, Netflix’s AI understands your preferences and curates a list that feels just right.

In this article, we’ll dive into how Netflix’s AI works its magic behind the scenes, creating a tailored viewing experience that keeps users engaged and always looking for more.

Introduction: Why Millions of Viewers Fell in Love with Netflix?

If we travel back to the early 2000s, the entertainment landscape was vastly different from what we see today. However, with the rise of entertainment giants like Netflix, people were given access to an immersive library of content, and thanks to its user-friendly interface, Netflix managed to capture the hearts of millions of viewers worldwide. Get to know more about AI in entertainment in our blog.

Netflix, founded in 1997 by Reed Hastings and Marc Randolph as a DVD rental service, transitioned to streaming services in the late 2000s as the rental business struggled to stay alive. In the early days of streaming, Netflix focused on licensing existing TV shows and films. However, the wind of changes came with the production of original content. Hits like “House of Cards,” “Stranger Things,” and “The Crown” proved Netflix is capable of creating compelling and high-quality series that resonate with viewers of all ages.

Take Stranger Things, for example. When the series launched, Netflix didn’t simply push it to fans of sci-fi. The platform’s system also recognized that viewers who liked nostalgic 80s themes and coming-of-age stories would be interested. This multi-layered approach helped Stranger Things become a global sensation, all thanks to Netflix’s use of AI.

But making personalized recommendations isn’t simple. With an enormous library of titles and millions of viewers with different tastes, Netflix faces the daunting task of predicting what each person will want to watch next. The solution lies in the power of artificial intelligence, which analyzes massive amounts of data—from your viewing habits to the types of shows you often skip—and uses that to fine-tune the suggestions it makes.

Yet, the numbers can describe Netflix’s presence and influence on the audience better than words.  As of 2023-2024, Netflix boasts over 260 million subscribers globally in over 190 countries, with the largest share in Europe and America. Interestingly, users spend an average of 3.2 hours each day consuming the content on the platform. The audience is 51% female and 49%  male. 

But why does it attract so many users? The two main reasons are:

• The great range of licensed TV shows and original productions, with the top show, The Night Agent, gaining around 812 million viewed hours;
• Clever use of AI to provide the best viewing experience.

Even now, when Netflix is proudly positioned as one of the leading OTT (over-the-top) platforms, the company hasn’t stopped searching for new ways to improve its quality and competitive edge with investments in cutting-edge technology like machine learning and artificial intelligence. With the vast AI experience in mind, Netflix has stated its desire to spend 8 billion dollars for this year’s content production.  

Netflix and AI

The company’s path to applying AI started by adapting to changing viewer preferences and embracing new technologies that will keep Netflix atop the competitive streaming market. Today, a mix of artificial intelligence, data science, and machine learning aims to engage all audiences with the movies they most prefer.

With the global market for OTT services predicted to jump to $1,079.1 billion by 2030, the further deployment of AI technologies is inevitable. This key market driver requires better user experience, high-speed loading, feature recommendations, and fast problem-solving services.

The real story of Netflix AI recommendations started with the production of House of Cards and the need to promote it to a large audience segment. By integrating AI, ML, and data science, the tech team worked on learning consumer behavior, preferences, and watch patterns. Based on the researched data, Netflix released ten totally different trailers for House of Cards that could attract a variety of audiences. How does it work?

The idea is the consumer will see exactly what he or she likes. For instance, a female consumer saw the trailer with the frame of her favorite actor, while another consumer saw the frame that demonstrates her preferred type of story plot. This technique helped the audience find the show and encouraged them to watch it.

By smartly implementing artificial intelligence, Netflix has proven that computer science attracts more users to its streaming service and can adjust consumer behavior.  But, the most remarkable discovery comes with applying an AI recommendation engine and increasing the platform’s revenue.

The Scale of Netflix’s Personalization Challenge

Netflix caters to over 282 million subscribers worldwide, each with unique tastes, preferences, and viewing habits. This immense user base presents a significant challenge: how can Netflix ensure that every individual finds content they love in its vast and ever-expanding library? With tens of thousands of titles spanning countless genres, languages, and formats, the task of personalization becomes both a technical and creative endeavor.

Massive Content Library

The sheer volume of content available on Netflix is staggering. From critically acclaimed originals like Stranger Things and The Crown to classic movies, documentaries, and international hits, the platform’s offerings appeal to a wide variety of audiences. However, this abundance can lead to choice paralysis—users may spend more time browsing than watching. Personalization solves this by narrowing down the options to those most relevant to the viewer.

Diverse and Global User Base

Netflix’s audience is incredibly diverse, spanning continents, cultures, and languages. A user in South Korea may enjoy romantic K-dramas, while a subscriber in Mexico might prefer action-packed thrillers. Even within the same household, family members often have distinct preferences. Netflix using AI not only has to accommodate these differences but also adapt to changing tastes over time. For instance, someone who previously enjoyed comedies might suddenly develop a penchant for psychological thrillers. The system must dynamically evolve with the viewer’s preferences.

Dynamic User Behavior

Adding to the complexity is the variability in how users engage with the platform. Some binge-watch entire series in one weekend, while others return sporadically to watch a single movie. Additionally, context matters—what someone wants to watch on a Friday night might differ significantly from their choice on a lazy Sunday morning. Netflix’s AI personalization engine must account for these nuances to deliver recommendations that resonate in the moment.

By addressing these challenges through cutting-edge AI, Netflix transforms its scale and diversity into strengths, ensuring that users always discover something they want to watch. This delicate balance of catering to individual preferences while managing an immense library is what sets Netflix apart in the competitive streaming landscape.

How Netflix Uses AI: Netflix Recommendations Algorithm

AI is changing the world by using data science research to enhance the user experience. 

Netflix’s AI recommendation engine analyzes massive amounts of data, including viewing habits, ratings, searches, and time spent on the platform, to curate personalized content recommendations for each viewer.

Now let’s split the whole Netflix recommendations algorithm into a more simplified process so we can digest it and understand the whole idea:

• Netflix, using artificial intelligence, identifies what you watch, how many times you click the same video, and how long you keep watching it;
• Based on the initial preferences of the user, Netflix identifies the preferred types of movies and shows using the following factors:
• The kinds of movies you watched previously
• Whether you finished watching previous shows/movies
• How quickly have you watched all episodes of a series
• What movies and shows are watched now by users with the same preferences
• Next, the AI rates preferred shows and movies based on their popularity rankings and creates a list of recommendations for what to watch next.
• As in the above step, new recommendations keep appearing on the playlist based on the previous watching history and similar popular ratings.

This amazing algorithm is simply called Netflix AI, the product of technologies and entertainment.

Use Cases of AI/ML/Data Science in Netflix

Netflix’s commitment to providing excellent user experience and engaging more viewers to the platform is elevated by its use cases and best practices. By understanding the power of new technologies, businesses can integrate AI to enhance content, customer support and communication, behavioral predictions, and increase ratings. 

• Netflix Machine Learning

While machine learning helps to process recommendation lists, it also helps to find the most suitable shooting locations for upcoming movies and shows.  For instance, the system learns the locations with the less rain likelihood for any day of the week and other factors helpful in problem-solving and decreasing costs under certain circumstances.

Moreover, with the help of machine learning and data science, teams can now easily determine the schedule and cost of the cast crew.

Furthermore, machine learning allows tech teams to monitor users’ activities and viewing patterns in real-time to determine the platform’s peak hours. The obtained data is used to cache the local servers and speed up loading.

• Deep Learning

Deep learning allows Netflix to uncover complex, non-linear relationships in data that simpler algorithms might miss. This is particularly useful for analyzing high-dimensional data, such as:

Neural networks analyze intricate patterns in how users interact with the platform, including pause/resume behavior, replays, skips, and even binge-watching patterns.

Deep learning helps identify subtle features within movies and shows, such as visual style, dialogue pacing, and soundtrack mood. This allows Netflix to go beyond metadata and understand the deeper essence of content.

For example, if a user tends to prefer fast-paced, action-packed thrillers, deep learning models can recommend not just similar genres but content that shares similar pacing or intensity.

•  Natural Language Processing (NLP)

NLP plays a crucial role in processing text-based information, helping Netflix understand both user intent and content attributes. Key applications include:

AI in Netflix analyzes the language used in descriptions and tags to classify tone, themes, and moods. For example, “gritty crime drama” and “lighthearted family comedy” evoke very different viewing expectations.

NLP models extract sentiment and keywords from user feedback to refine recommendations. For instance, if many users describe a show as “fast-paced” or “emotionally moving,” the algorithm prioritizes these traits in its suggestions.

Netflix’s AI analyzes natural language search inputs to interpret intent, ensuring relevant and personalized results are returned.

• Personalization & Search

Its AI-driven personalization and search capabilities are at the heart of Netflix’s success. Without personalization and AI recommendations, the viewer won’t be attracted by the appeal of the next series and won’t stay on the platform for as long.

Despite an extensive library of content, all users have their own tastes and preferences: some are looking for fantasy, some are obsessed with trending K-drama, and some prefer movies only with their favorite actors. In other words, not every user will click the first movie they see to find out whether they would like it. 

To resolve this issue, Netflix uses AI to analyze users’ watching history and time spent on the show to select similar movies that the user will enjoy. The result is personalized, engaging content.

• Content-Based Filtering

This technique focuses on the attributes of the content itself. Netflix uses metadata tagging to classify movies and shows based on elements like:

• Genres: Comedy, horror, romance, etc.

• Themes: Coming-of-age stories, dystopian settings, underdog triumphs, etc.

• Cast and Crew: Recognizing actors, directors, and production teams to suggest content with familiar names.

Additionally, AI in Netflix employs video and audio analysis to extract insights from the content directly. For instance, the system can analyze a scene’s color palette, camera angles, or soundtrack to categorize content more granularly. This enables recommendations that go beyond surface-level similarities.

• Reinforcement Learning

Reinforcement learning helps Netflix fine-tune its recommendation system by focusing on real-time user feedback. The platform continuously monitors interactions, such as:

• Play Actions: What users choose to watch.

• Skips: What they avoid or abandon.

• Ratings and Thumbs Up/Down: Explicit feedback on content.

The system assigns rewards to recommendations that result in positive outcomes, such as a user completing a series or rating it highly. Over time, reinforcement learning ensures the algorithm learns what works best for each user, dynamically adapting to their changing preferences.

• Auto-Generated Thumbnails

Netflix AI works perfectly by examining thousands of video frames and capturing the one frame that is most appealing to the viewer—the thumbnail. Next, this thumbnail is displayed on the movie recommendation list, increasing the likelihood of people clicking on the movie or show.

Moreover, thumbnails appear on the playlist not randomly but based on the clicking rates of other viewers with the same interests. If you are addicted to Netflix, you have probably noticed the same pattern over and over again. That’s how Netflix identifies which thumbnail works best for you.

• Streaming Quality 

Another critical case of AI for Netflix is optimizing streaming quality. Through ML algorithms, Netflix continuously monitors network conditions and adjusts video quality in real-time to ensure a seamless viewing experience. 

By analyzing bandwidth, device type, and location data, Netflix can deliver the best possible video quality while minimizing buffering and interruptions. As a result, people are likely to stay on the platform due to high-speed performance and faster content loading.

• Content Quality Checks

Netflix uses AI to identify and categorize content based on various factors, including genre, language, and maturity rating. This enables the platform to uphold quality standards and ensure content meets viewer expectations. As a result, the Netflix recommendation engine promotes high-quality and relevant content to users, enhancing the overall viewing experience.

Benefits of Netflix AI

Netflix’s artificial intelligence recommendations benefit both viewers and Netflix itself, paving the way for a more engaging and satisfying entertainment experience. Let’s make a breakdown and see the results of AI magic. 

For Netflix:

1. Increased Engagement. By providing personalized recommendations, Netflix can keep viewers on the platform longer, reducing exit rates and increasing customer satisfaction. This leads to higher viewer engagement and more time spent watching content.
2. Keyword Upscale. By tagging films and TV shows with keywords related to genre, theme, cast, and more, Netflix’s algorithm can identify patterns and similarities between content and recommend relevant titles to viewers. This way, recommendations are more accurate, diverse, and appealing to a wide range of people.
3. Content Promotion and Discovery. Netflix’s AI recommendations let viewers explore new genres, discover hidden gems they have yet to notice, and broaden their entertainment horizons. As a result, more traffic is attracted to the platform.

For viewers:

1. Personalized Experience. Instead of sifting through a vast library of content to find something to watch, viewers are presented with a curated selection of movies and TV shows that align with their interests and preferences. This not only saves time but also enhances the overall viewing experience. 
2. Discovering the Next Big Show. If you are a big fan of watching movies and TV shows, you don’t have to spend hours searching for the next big hit. Netflix will recommend all possible great shows for you according to your previous viewing history. Indeed, Netflix’s AI feature saves tons of time.

Conclusion

The rise of Netflix AI can be attributed to its innovative approach, diverse content library, and user-centric focus. By focusing on personalization, streaming quality, and content quality checks, Netflix continues to set the standard for digital streaming platforms.

Adopting best practices in machine learning, data science, and AI yields incredible results. By embracing Netflix’s success, businesses can maximize their turnover, increase leads, and improve customer experience. If you want to learn more about your business potential, contact our team, and we will share our expertise in AI solutions.

Finally, let’s give a round of applause to Netflix for its amazing user experience and unbelievable movies. Now, let’s go watch some Netflix!