A Must Read Case Study on Data Science at Netflix for Aspiring Data Scientists

A Must Read Case Study on Data Science at Netflix for Aspiring Data Scientists

bharani bharani

2 months ago

Netflix uses data science to provide you with relevant and engaging recommendations. Thus, we will talk about the same in this article today. Let's start our Netflix Data Science inquiry with a brief streaming service review.


Netflix launched in 1998 as a DVD rental service. The company typically used a third-party postal service to get its DVDs to users. As a result, they faced considerable losses, which were rapidly mitigated when they established their internet streaming company in 2007.

Netflix invested much in algorithms to provide its customers with a flawless movie experience. One of these algorithms is Netflix's recommendation system, which provides ideas to customers. A recommendation system anticipates user requests and suggests various cinematic things. Explore the data science certification course, which includes 15+ real-world projects.


What is the Definition of a Recommendation System?


A recommendation system is a platform that serves up varied content to its consumers depending on their preferences and likings. A recommendation system uses the user's information as input.


This information can take the shape of previous product usage or product ratings. It then employs this information to forecast how highly the buyer will rank or like the product. A recommendation system uses data science techniques. Another key function of a recommendation system today is to look for similarities between different products. In the case of Netflix, the recommendation algorithm looks for movies similar to those you have previously viewed or loved.


This is a useful strategy for cold start conditions. During the cold start, the company does not have much user data to generate recommendations. As a result, based on the movies that are watched, Netflix recommends films that are comparable in some way. There are two kinds of recommendation systems:


Recommendation systems based on content:


Background product knowledge and client information are considered in a content-based recommendation system. It makes comparable recommendations based on the content you've watched on Netflix.

For example, if you view a film in the sci-fi genre, the content-based recommendation system would offer you comparable films in the same genre.


Recommendation Systems for Collaborative Filtering:


Unlike content-based Filtering, which recommends related products, Collaborative Filtering recommends products based on similar user profiles. One significant benefit of collaborative Filtering is that it is not dependent on product expertise.

Instead, it relies on consumers, with the basic idea that what users loved in the past would also like in the future. For example, if A enjoys crime, sci-fi, and thriller genres and B enjoys sci-fi, thriller, and action genres, A will enjoy activities, and B will enjoy the crime genre.


A third kind of recommendation system incorporates both Content and Collaboration approaches. This type of recommendation system is referred to as a Hybrid Recommendation System. Netflix recommends content to its customers primarily through the Hybrid Recommendation System. Check out the data science course fees offered.


How Netflix Used Data Science to Address Its Recommendation Issue?


When Netflix first decided to enter the streaming industry in 2006, it began with a movie rating prediction competition. It offered a $ 1 million prize to anyone who improved the accuracy of their then-existing platform, 'Cinematch', by 10%. At the end of the competition, the BellKor team submitted their solution, which boosted prediction accuracy by 10.06%. This outcome was achieved after over 200 hours of work and an ensemble of 107 algorithms. Their final model's RMSE was 0.8712. They used the K-nearest neighbor technique for data post-processing in their solution.


Then they built a factorization technique known as Singular Value Decomposition (SVD) to provide its consumers with the best dimensional embedding. They also used Restricted Boltzmann Machines (RBM) to improve the collaborative filtering model's capacity. The ensemble's SVD and RBM algorithms produced the best results. The RMSE was decreased to 0.88 using a linear combination of these two techniques.


However, despite reducing RMSE and increasing accuracy, Netflix faced two big problems. First, the data offered during the competition consisted of 100 million movie ratings instead of Netflix's more than 5 billion ratings. Additionally, the algorithms were static, which meant they only dealt with past data and ignored the dynamic nature of consumers contributing reviews in real time. After overcoming these obstacles, Netflix included the winning algorithms in its recommendation system.


Improving Personalization Via Interleaving:


Netflix uses Ranking Algorithms to present a ranked list of movies and television shows that are popular among its consumers. Yet, due to the presence of multiple ranking algorithms, it is frequently impossible to accommodate all of them and test their performance at the same time. Because traditional A/B testing on a limited range of algorithms failed to uncover the optimal algorithms with a smaller sample size and required a significant amount of time, Netflix chose to improve its algorithmic process.


Netflix used the interleaving technique to determine the best algorithms to speed up its ranking algorithms' experimentation phase. This technique is used in two steps to deliver the best page ranking algorithm to its users to provide customized suggestions. Get to know about the data scientist course fees.


Experiments to determine member preference between the two ranking algorithms are carried out in the first step. Unlike A/B testing, in which two sets of viewers are exposed to the two ranking algorithms, Netflix uses interleaving to combine algorithm A and B rankings. Netflix distributes richer material to its users based on this interleaving approach, particularly sensitive to algorithm quality ranking.


Context Awareness is Important in Suggestions:


Contextual Awareness is an important factor in personalizing recommendations for its customers.

This not only enhances the recommendation system's effectiveness but also encourages users to submit better input, resulting in a quality recommendation. There are two kinds of contextual classes:


  • Explicit:
  • Device 
  • Location
  • Language
  • Time of Day


  • Implicit:
  • Companion
  • Binging Patterns


We employ representation learning to predict contexts. It is a deep learning feature engineering technique that finds features without explicit coding.

Netflix bases its data on many parameters such as Day, Week, Season, and even longer periods such as the Olympics, FIFA, and elections.

To predict contexts, we use representation learning. It is a deep learning feature engineering technique that discovers features without explicit programming. Netflix bases its data on various parameters, including Day, Week, Season, and even longer periods, such as the Olympics, FIFA, and elections.




In this blog, we examined how Netflix uses a recommendation engine to deliver movie recommendations to its subscribers. We also investigated how Netflix extensively relies on Data Science tools to provide useful recommendations. Further, we discussed the Netflix Prize competition and how it uses the winning team's algorithms to increase accuracy. Finally, we talked about contextual prediction and how Netflix uses it to deliver personalized recommendations to its subscribers.


I hope this data science case study helped you better understand Data Science. You should read these to understand data analytics better. Also, you can have a look at the best data analytics course online to learn more about data-related tools and techniques.


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