How Netflix Knows What You Will Watch Next

Netflix is undoubtedly one of the biggest streaming platforms in 2025. Today, we will examine how Netflix’s recommendation system works, along with other similar algorithms that analyze your data and predict your preferences.

This article is designed to be beginner-friendly and does not contain detailed technical content. It can be easily understood by everyone without significant effort.

Your Simple Interactions Drive Recommendations

Your interactions on Netflix are recorded (which can be a bit concerning for those who value privacy, but that’s not our focus today). They collect simple data such as your viewing history, ratings, genres, and categories you’ve watched.

Ultimately, what you’ve watched serves as the best starting point for recommending the next movie or series. While most people enjoy various categories, they tend to gravitate toward one in particular.

Besides that, they also collect some personal data like how much time you spend on Netflix, your preferred languages, the device you use, and how long you watch each title, etc.

Next Step: Feeding the Algorithm with Data

While Netflix’s algorithm is not publicly available, we can make educated guesses about what they use. However, this is not certain, as Netflix’s algorithm is unique, just like those of other social media platforms.

Basic algorithms use time-based and category-based recommendations. For example, they analyze the titles you spend the most time on, extract their categories, and, thanks to machine learning algorithms, predict the next movie that is likely to be suitable for you.

One of the easiest solutions is using the content-based filtering method. This approach recommends items based on their features and the user’s preferences. For instance, if you enjoy action movies, the system will suggest other action movies that share similar traits.

How the Algorithm Makes Recommendations for New Users?

We discussed the data and how it’s processed for recommendations, but how does Netflix predict what we will watch right after we install the app?

Simple! A few titles that you select before creating an account help the algorithm understand your preferences. Before account creation, you need to choose three titles, which assists the algorithm in making recommendations.

However, algorithms are not perfect, so they may not be very effective at making recommendations based on just a few titles. The longer you use the app, the more data it collects, which generally leads to better results, though there can be exceptions.

Personalised Homepage

The homepage is the first place you visit when you open Netflix, and it should capture your attention to keep you engaged with the app. Therefore, Netflix designs its homepage to be as personalised as possible to attract your interest.

The first thing you see when you open the app is the ‘Continue Watching’ section, which allows you to pick up right where you left off in a series. This feature helps prevent you from wasting time searching for the same title again.

Below that, you’ll find recommended rows and category rows that are personalised based on your data.

Spicy: Trying Out Content-Based Filtering in Python

Now that we understand the basics of Netflix’s prediction system (although Netflix’s algorithm is much more advanced than this), it’s time to see how it works by trying it out in Python.

Attention: In this section, I assume you have a basic understanding of Python, as well as the scikit-learn and pandas libraries.

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

In here we will use pandas just to create DataFrames which we will operate on data. TfidfVectorizer is simple import that helps us converting text documents into numerical feature vectors using the TF-IDF method.

Finally, cosine_similarity calculates the similarity between the TF-IDF vectors of the text documents. It measures how closely related the items are based on their descriptions. The similarity score ranges from 0 to 1, where a score of 1 means the items are very similar, and a score of 0 indicates no similarity.

Data = {
    'Title': ['Movie A', 'Movie B', 'Movie C', 'Movie D'],
    'Description': [
        'Action film',
        'Romantic comedy',
        'Action-packed thriller',
        'Romantic drama'
    ]
}

Df = pd.DataFrame(Data)

Now that we have a simple dataset with four movies, we can perform our content-based filtering on this dataset next.

# Create a TF-IDF Vectorizer
TfidfVectorizerInstance = TfidfVectorizer(stop_words='english')
TfidfMatrix = TfidfVectorizerInstance.fit_transform(Df['Description'])

# Compute the cosine similarity matrix
CosineSim = cosine_similarity(TfidfMatrix, TfidfMatrix)

Now we can make a funciton to get recommendations of movies.

def GetRecommendations(Title):
    Idx = Df.index[Df['Title'] == Title].tolist()[0]
    SimScores = list(enumerate(CosineSim[Idx]))
    SimScores = sorted(SimScores, key=lambda x: x[1], reverse=True)
    SimScores = SimScores[1:4]
    MovieIndices = [i[0] for i in SimScores]
    return Df['Title'].iloc[MovieIndices]

The GetRecommendations function works by taking a movie title you provide and finding its index in the list. It then looks up how similar other movies are to your choice using the cosine similarity scores. After sorting these scores, it selects the top three most similar movies (excluding the one you picked) and returns their titles.

Trying Out Our Python Code

Now, let’s give it a try and see if it really works. Of course, we have a small dataset, and we will only provide one input, which will affect the accuracy, but that’s not the main focus here; we are just demonstrating the concept.

# Example: Get recommendations for 'Movie A'
print(GetRecommendations('Movie A'))

# Output
['Movie C', 'Movie D', 'Movie B']

Since Movie A is an action film, the function will look for the most similar movies based on their descriptions. In this case, Movie C (which is also an action film) is likely to be the most similar, followed by Movie D (which is a romantic drama) and Movie B (which is a romantic comedy).

Conclusion

Netflix and other social platforms are providing personalized services to keep you engaged with their offerings. It is true that personalized content consistently generates better interactions than generic content.

While this article does not fully cover everything big companies do in their algorithms—developing a fully functional Netflix algorithm would take years—it aims to give you a foundational understanding of how these companies leverage data to deliver the best content in real time.

By understanding these concepts, you can start to create personalized experiences for your users, ultimately enhancing their engagement and satisfaction with your offerings (or you can just have fun with the new knowledge).