2 m read

Random Forests: Netflix Customer Recommendations Improved by 20%


  • Random Forests is a machine learning algorithm that combines multiple decision trees to improve prediction accuracy.
  • Netflix implemented Random Forests and achieved a remarkable 20% improvement in customer recommendations.
  • In this article, we explore the concept of Random Forests and how founders can leverage this technology to enhance their startup’s customer recommendations.

What are Random Forests?

Random Forests is a machine-learning algorithm that uses decision trees to make accurate predictions.

It is an ensemble learning method that harnesses the wisdom of the crowd by aggregating the predictions of individual decision trees.

Each decision tree is trained on a different subset of the data, and the final prediction is determined by a majority vote or averaging of the predictions from the individual trees.

Netflix’s Use of Random Forests in Recommendation System

Netflix, the renowned streaming platform, implemented Random Forests to revolutionize its customer recommendation system.

By analyzing vast amounts of user data, such as viewing history, ratings, and preferences, Netflix trained a Random Forests model to predict the most relevant recommendations for each user.

This led to a remarkable 20% improvement in the accuracy and effectiveness of their customer recommendations, resulting in increased user engagement and satisfaction.

Can Random Forests benefit my startup?

Absolutely! Random Forests offer several advantages for startups looking to enhance their customer recommendations:

  1. Improved Accuracy: Random Forests combine the predictive power of multiple decision trees, resulting in more accurate recommendations for your customers.
  2. Handling Large Datasets: Random Forests can efficiently handle large volumes of data, making them suitable for startups with growing user bases and extensive customer information.
  3. Robustness to Noise and Outliers: Random Forests are robust against noisy and outlier data, ensuring more reliable recommendations despite data imperfections.
  4. Feature Importance: Random Forests provide insights into the importance of different features, helping founders understand the factors that drive customer preferences and behaviors.

Deepening Your Understanding of Machine Learning in Recommendation Systems

After exploring the impactful use of Random Forests in Netflix’s recommendation system, you may be inspired to delve deeper into the world of machine learning and its applications in creating effective recommender systems.

A valuable resource in this journey is the book "Building Recommender Systems with Machine Learning and AI."

This comprehensive guide illuminates the intricate processes behind constructing powerful recommendation engines using advanced machine-learning techniques.


Random Forests offers founders a powerful tool to enhance customer recommendations and drive user engagement.

Taking inspiration from Netflix’s success, incorporating Random Forests into your startup’s recommendation system can unlock new avenues for growth.


Leave a Reply