How Netflix Utilizes Data Science and Analytics to Improve its Streaming Services

Do you recall the last Netflix movie you watched? I don't want to know the name; just think about it: Were you recommended similar movies after watching the movie? How does Netflix know what you want? The key here is Data Science.

Netflix employs Data Science to make relevant and interesting recommendations to you. So, in this article, we will go over that in detail. Let's begin by learning about Netflix and Data Science.

In 1998, Netflix began as a DVD rental service. It primarily relied on third-party postal services to deliver DVDs to users. This resulted in significant losses, which were quickly mitigated with the launch of their online streaming service in 2007.

Netflix invested heavily in algorithms in order to provide its users with a flawless movie experience. The recommendation system that Netflix uses to provide suggestions to users is one such algorithm. A recommendation system recognizes the needs of the users and suggests various cinematographic products.

What is a Recommendation System? A recommendation system is a platform that serves up different content to its users based on their preferences and likings. A recommendation system uses the user's information as input.

This information is in the form of previous product usage or ratings. It then uses this data to predict how much the user will rate or prefer the product. A recommendation system employs a number of machine learning algorithms. Refer to the Machine learning course in Delhi for further details on recommendation systems and its uses.

Here's how Netflix solved its recommendation problem with data science

When Netflix first decided to enter the streaming market in 2006, it began with a movie rating prediction competition. It offered a $ 1 million prize to whoever improved the accuracy of their then-existing platform 'Cinematch' accuracy by 10%.

At the end of the competition, the BellKor team presented their solution, which increased prediction accuracy by 10.06%. This result was achieved after more than 200 hours of work and an ensemble of 107 algorithms.

The RMSE of their final model was 0.8712. They used the K-nearest neighbor algorithm for data post-processing in their solution.

Then they implemented a factorization model known as Singular Value Decomposition (SVD) to provide its users with the best dimensional embedding.

Context Awareness is Important in Recommendations Contextual Awareness is an important factor in personalizing recommendations for its users.

This improves the recommendation system's performance and encourages users to provide better feedback, resulting in a quality recommendation. Contextual classes are classified into two types:

Explicit Location Language Device

Inferred Time of Day Companion for Binging Patterns

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

Improving Personalization Through Inter-leaving Netflix employs ranking algorithms to provide a ranked list of movies and television shows that are popular with its users. However, due to the presence of multiple ranking algorithms, it is frequently difficult to accommodate all of them and test their performance at the same time.

While traditional A/B testing on a limited set of algorithms failed to identify the best algorithms with a smaller sample size and required significant time, Netflix decided to innovate its algorithmic process.

Netflix used the interleaving technique to identify the best algorithms to speed up its ranking algorithms' experimentation process. This technique is used in two stages to provide the best page ranking algorithm to its users to provide personalized recommendations.

Experiments to determine member preference between the two ranking algorithms are carried out in the first stage. Unlike A/B testing, in which two groups of viewers are exposed to the two ranking algorithms, Netflix uses interleaving to blend algorithm A and B rankings. Thus, Netflix provides enriched content to its users based on this interleaving technique, which is highly sensitive to algorithm quality ranking.

I hope this article gave you a quick insight into how Netflix utilizes data science and AI to improve its services. For more details on data science, visit the IBM-accredited Data science course in Delhi, where you can learn directly from industry tech leaders via online classes.