How does Artificial Intelligence work in Netflix?

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Artificial Intelligence in Netflix

The role of Artificial Intelligence across sectors and industries is becoming universal — especially when large companies like Netflix, Amazon, Twitter, Spotify, and many more continually deploy AI-related solutions that interact directly with customers daily. Such AI-related approaches can provide truly unique solutions that grow and improve over time, having a significant impact on both company and user, if correctly applied to business problems. In this blog-post, we’re going to look at how Netflix is consuming AI to serve its users with the best-customized features.

How does Artificial Intelligence work on Netflix?

 

  • Film Recommendations & Personalization using Artificial Intelligence

The problem here is that Netflix has a vast content collection (more than 100 million different products, according to Netflix), which is continually changing and can be overwhelming for a user to consume. When seeking content relevant to their interests, consumers don’t want to be disappointed. So what is the best way for each user to access the data to improve subscription loyalty? Netflix analyses the data of users having similar interests to recommend movies & shows.

  • Auto-Generating and Customizing Thumbnails/Artwork:

Thumbnail is the first thing that provokes the user to watch a specific show. Therefore, Netflix customizes various thumbnails using Artificial Intelligence for the movies & shows that a user might like it. The customization is done after analyzing the user profile, past watching history, interest & browsing activity.

Netflix annotated these images using thousands of video frames from an existing movie and using these as the starting point for thumbnails generation and then rated each image to decide the thumbnails you most likely want to make. These calculations are based on what has been clicked on by others, similar to you. One result might be that users who like certain actors/film types with specific actors/image attributes are more likely to click thumbnails.

  • Place Scouting for Movie Production (Pre-Production)

 

This is a customization feature driven by data that sits on top of the recommendation engine for Movie. Netflix uses data to help determine where and when to film a movie set best — considering the scheduling constraints (actor/crew availability), the budget (run, flight/hotel costs) and the conditions of the production scene (day vs. night shooting, the possibility of weather events in a place). Remember that this is more a question of optimizing data science than a system of machine learning that makes predictions based on past data.

  • Film Editing

Netflix uses historical data to ensure that the subtitles are matching up with the speed of the sound of the video. These data also enable Netflix when the synchronization of subtitles to sound/movements was off in the past. They use these data to edit the films & thus improve the overall user experience.

  • Streaming Performance

Use past viewing data to predict the use of bandwidth to help Netflix determine when to cache local servers at peak (expected) demand for faster load times.

Conclusion

Netflix has done a remarkable job of applying the “right way” to Artificial Intelligence, Data Science, and Machine Learning — using a product-based approach that focuses on business needs first. AI solution first, not the other way around. Artificial Intelligence can do wonders when applied correctly. As far as overall satisfaction is concerned, we have seen how powerful AI technology can be in personalizing the experience for both users and Netflix.

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