AI News, Optimizing the Netflix Streaming Experience with Data Science

Optimizing the Netflix Streaming Experience with Data Science

by Nirmal Govind On January 16, 2007, Netflix started rolling out a new feature: members could now stream movies directly on their browser without having to wait for the red envelope in the mail.

Netflix is committed to delivering outstanding streaming service and is investing heavily in advancing the state of the art in adaptive streaming algorithms and network technologies such as Open Connect to optimize streaming quality.

In this post, I will briefly outline the types of problems we’re solving, which include: User behavior refers to the way users interact with the Netflix service, and we use our data to both understand and predict behavior.

Since network capacity is limited, picking too high of a bitrate increases the risk of hitting the capacity limit, running out of data in the local buffer, and then pausing playback to refill the buffer.

With vast amounts of data, the mapping function discussed above can be used to further improve the experience for our members at the aggregate level, and even personalize the streaming experience based on what the function might look like based on each member’s “QoE preference.” Personalization can also be based on a member’s network characteristics, device, location, etc.

With millions of members, a large catalog, and limited storage capacity, how should the content be cached to ensure that when a member plays a particular movie or show, it is being served out of the local cache/appliance?

Given our large and varied catalog that spans several countries and languages, the challenge is to ensure that all our movies and shows are free of quality issues such as incorrect subtitles or captions, our own encoding errors, etc.

This data can be very noisy and may contain non-issues, issues that are not content quality related (for example, network errors encountered due to a poor connection), or general feedback about member tastes and preferences.

Machine learning models along with natural language processing (NLP) and text mining techniques can be used to build powerful models to both improve the quality of content that goes live and also use the information provided by our members to close the loop on quality and replace content that does not meet the expectations of Netflix members.

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