I Broke Down Netflix’s Recommendation System So You Don’t Have To

Recommendation systems aren’t magic — they’re layered engineering decisions disguised as AI. I break down how Netflix-style recommenders actually work, the tradeoffs nobody mentions, and how I think about building them in the real world.

I Broke Down Netflix’s Recommendation System So You Don’t Have To

Introduction

I first got obsessed with recommendation systems when I realized Netflix kept predicting my “mood” better than my friends.

Most people think recommendations are just “AI magic” or deep learning hype — that’s lazy thinking

By the end of this post, you’ll understand:

• The real building blocks behind recommendations
• Why Netflix-style systems are more engineering than ML flexing
• How to think about recommender systems like a builder, not a researcher

What People Think Recommendation Systems Are (And Why That’s Wrong)

Most people (including my previous self) think recommendation systems are:

• Just a fancy search engine
• Maybe a fancy neural network trained on billions of data points
• Some black-box AI that “understands” users

But I was completely wrong.

Here’s what recommendation systems actually are:

• It is a really simple ML problem dressed up in layers of engineering
• It is about ranking items, not predicting exact preferences
• It is about signals from user behavior, not deep understanding of users

For instance, the Netflix recommendation system.

It is not a neural network that "knows you" or "spies on you".

Instead it is a layered ML system that processed your viewing history, search history, watch times, your binge-watched series, your half left series and so on.

Then it tries to rank multiple show you might like to watch next, instead of predicting exactly what you want to watch.

Let us reflect on this for a moment.

The ranking part is crucial.

It means that the system is not trying to predict your exact preference, but rather it is trying to order the shows based on your past behavior and other signals.

In any real world scenario, this is a much more practical approach.

It allows the system to be flexible and adapt to changing user preferences over time.

This even shows that the system is not trying to "understand" you in a deep sense, but rather it is trying to make educated guesses based on your behavior.

Remember, $\text{Data} > \text{Models}$.

I might have tried fitting a neural network model, but without the right data and signals, it would have been useless.

The real magic of recommendation systems lies in the engineering behind them.

It is about how you collect, process, and use data to make informed decisions about what to recommend next.

It also has a feedback loop.

In essence, it's a small rewarding system like if the algorithm recommends a show and you watch it, it gets a reward signal that the recommendation was good.

If you skip it, it gets a negative signal.

This feedback loop is crucial for improving the recommendations over time.

The Mental Model I Use: Signals, Not Users

• Why Netflix doesn’t “know you” — it tracks signals
• Explicit vs implicit signals (and why implicit wins)
• Thinking in terms of matrices, not personalities

If you delve on the thoughts you might start to realize that Netflix was never trying to "know you", rather it was trying to track your behavior and use that to make recommendations.

Now here signals are crucial and are classified into 2 types:

• Implicit signals: These are the signals that are derived from your behavior. For example, what shows you watch, how long you watch them, what you search for, etc.
• Explicit signals: These are the signals that you provide directly. For example, ratings, reviews, surveys, etc.

Netflix relies on implicit signals more than explicit ones.

Why?

Because implicit signals are more abundant and easier to collect.

Most users don't rate shows or provide feedback, but they do watch shows and interact with the platform.

My proffessor used to say: "ML is all about matrices".

Netflix (I believe) also had this thought somewhere.

It modelled you as a matrix and tried to find patterns in that matrix to make recommendations.

You were never a personality to Netflix systems, but rather a set of signals in a matrix.

So much for the theory, now let us see how Netflix actually builds its recommendation system.

The Three Layers of a Netflix-Style Recommendation System

Step 1: Candidate Generation

In this step, Netflix narrows down the vast library of content (millions of titles) to a manageable set of candidates (hundreds).

This is done using techniques like collaborative filtering, content-based filtering, and popularity-based methods.

• Collaborative filtering: It looks at what similar users have watched and liked. It is really bad for new users as they haven't yet watched anything and maybe classified in the wrong cluster. It is a heavy cold start problem.

• Content-based filtering: It looks at the attributes of the content you have watched and finds similar content. For example, if you watch a lot of sci-fi shows, it will recommend more sci-fi shows. This fixes our cold start problem to some extent. If you are a new user, and you watch let's say Interstellar, it will start recommending more movies and shows in the sci-fi genre.

• Popularity-based methods: It recommends content that is popular among all users or within specific segments. This is a safe bet for new users as popular content is more likely to be liked by a wide audience.

So if you are a new user, Netflix will rely more on content-based filtering and popularity-based methods to recommend shows.

Step 2: Ranking

Once the candidates are generated, Netflix ranks them based on various factors like user preferences, context (time of day, device, session length), and business goals.

This is where machine learning models come into play.

They use techniques like learning-to-rank, gradient boosting, and deep learning to order the candidates.

Now Netflix does not just rank based on what you like, but also based on context.

For example, if you are watching on a mobile device during your commute, it might recommend shorter shows or episodes.

If you are watching on a big screen at home, it might recommend longer movies or series.

Step 3: Post-Processing

Finally, Netflix applies business rules and filters to the ranked list.

This includes things like diversity (to avoid recommending too many similar shows), freshness (to promote new releases), and content availability (to ensure the recommended shows are available in your region).

Why Context Beats Accuracy

Context is king in recommendation systems.

Netflix understands that your preferences can change based on various factors like time of day, device, and session length.

For example, you might prefer light-hearted comedies during your lunch break but want to binge-watch a serious drama on a lazy Sunday evening.

You are more likely to watch a small episode on your phone during a short break than a 2-hour movie.

Netflix takes this into account when ranking recommendations.

It models your daily usage with phone vs TV and adjusts recommendations accordingly.

You might also want to explore new genres or shows that you haven't watched before.

Netflix balances personalization (recommending what you like) with exploration (introducing you to new content) to keep the experience fresh and engaging.

It is a delicate tradeoff, but Netflix has mastered it through continuous experimentation and user feedback.

How I Actually Use This

I use this mental model to design recommendation systems for my own projects.

Here are some key takeaways:

• Start simple: Use heuristics and rules before jumping into complex ML models. For example, recommend popular items or items similar to what the user has interacted with.

• Focus on ranking: Treat recommendations as a ranking problem rather than trying to predict exact preferences. This allows for more flexibility and adaptability.

• Log everything: Collect as much data as possible about user interactions, context, and feedback. This data is crucial for improving recommendations over time.

It also teached me to treat this problem as a regression problem rather than a classification problem.

Instead of trying to predict whether a user will like an item or not, I try to predict a score or ranking for each item based on user behavior and context.

What I’d Do Differently If I Started Today

If I were to start building a recommendation system today, I would focus on getting a working prototype up and running quickly.

I would use simple heuristics and rules to generate recommendations based on user behavior and context.

I would delay the use of complex ML models until I have enough data and scale to justify their use.

I would optimise it for iteration speed rather than theoretical accuracy.

This means I would focus on getting feedback from users quickly and iterating on the system based on that feedback.

I would also prioritize logging and data collection to ensure that I have enough data to improve the recommendations over time.

Common Mistakes & Gotchas

Sometimes, teams get obsessed with optimizing for offline metrics like RMSE or precision/recall.

But these metrics don’t always translate to better user experiences.

It is crucial to validate recommendations with real user feedback and engagement metrics.

Never ever neglect the feedback loop.

If users feel overwhelmed or fatigued by recommendations, they might disengage from the platform altogether.

It is important to monitor user satisfaction and adjust recommendations accordingly.

More data is not always better.

Quality matters more than quantity.

It is better to have a smaller set of high-quality data than a massive amount of noisy or irrelevant data.

Mini FAQ

• Q1. How does Netflix recommend movies and shows? It combines user behavior signals, content metadata, and ranking models — not a single algorithm.

• Q2. Is machine learning required for recommendation systems? No. Rules and heuristics get you surprisingly far before ML becomes necessary.

• Q3. What algorithm does Netflix use for recommendations? Multiple. Collaborative filtering, matrix factorization, learning-to-rank — all layered.

• Q4. How do recommendation systems handle new users? They lean heavily on popularity, metadata, and early-session behavior.

• Q5. Are recommendation systems the same as search? No. Search responds to intent. Recommendations guess intent.

My Opinion: When to Build Netflix-Style Recommenders

My honest take: most teams overbuild recommendation systems way too early

This is NOT for:

• Teams without enough user interaction data
• Builders looking for plug-and-play “AI”

This approach breaks when:

• Feedback loops aren’t monitored
• Business incentives quietly override user value
• Scale introduces latency and infra complexity

If you’re building anything user-facing, you’re already designing a recommender — whether you admit it or not.

Try breaking down your product into signals and ranking layers.

If this clicked, you’ll probably enjoy my upcoming deep dives on real-world ML system design and engineering.

Credible Sources

1. Netflix Tech Blog
2. Google Machine Learning – Recommendation Systems Guide
3. “Matrix Factorization Techniques for Recommender Systems”

Subscribe to Our Newsletter