Dataconomy
  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
Subscribe
No Result
View All Result
Dataconomy
  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
Subscribe
No Result
View All Result
Dataconomy
No Result
View All Result

How Netflix is Using Data to Improve Quality of Experience

by Eileen McNulty
June 13, 2014
in Data Science, News
Home Topics Data Science
Share on FacebookShare on TwitterShare on LinkedInShare on WhatsAppShare on e-mail

We recently reported on how Netflix is using big data algorithms to power its recommendation system; now, a Netflix blog post highlights other ways in which the company is using big data behind the scenes to enrich its service. They’re putting the data to work largely to improve Quality of Experience (QoE)- the experience the user has after they hit play.

The blog post, written by Director of Streaming Science & Algorithms Nirmal Govind, walks us through several ways in which data is enhancing the quality of experience. The first approach outlined is looking at “the algorithms that run in real-time or near real-time once playback has started, which determine what bitrate should be served, what server to download that content from, etc.”

“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'”, he continues. “Personalization can also be based on a member’s network characteristics, device, location, etc.” For instance, a member using a computer with a high-bandwidth connection will probably have very different quality expectations to someone watching on their mobile using a low-bandwidth network. They’re also looking into algorithms to optimise content location so it’s as close to end user (in terms of network hops) as possible.

Although in our last report we mentioned Netflix weren’t going to use their data when producing the content, but there is one way in which content is affected by data- in the field of subtitles, audio and closed captions. Given the amount of countries Netflix is now available in, subtitles and captions can have a huge impact on user’s opinion of the quality of the service. The first port of call when checking the quality of captions is user feedback- but relevant feedback is mired in an ocean of feedback that’s not related to content (e.g. network issues), non-issues, or comments that only relate to a user’s tastes and preferences. As Govind states, “identifying issues that are truly content quality related amounts to finding the proverbial needle in a haystack”.

Netflix’s solution is a model which predicts which content might have quality issues. The model detects patterns such a sharp drop-off points at a particular time in a show, and then couples this with feedback on that show to identify problems. They’re also using natural language processing and text mining techniques to improve the quality of captions and subtitles before they go live- a tool which will prove vital as Netflix continues to expand internationally.


Join the Partisia Blockchain Hackathon, design the future, gain new skills, and win!


The recommendation system is certainly the most well-known of Netflix’s data-based applications. But getting the user to hit play is only half the battle; if the quality doesn’t meet expectations in terms of speed, image quality and the accuracy of captions, users are going to click off. The techniques outlined above are trying to ensure that data improves the user experience right up until the credits roll.

Read more here.
(Image credit: Jovino)

Follow @DataconomyMedia


Interested in more content like this? Sign up to our newsletter, and you wont miss a thing!

[mc4wp_form]

 

Tags: NetflixWeekly Newsletter

Related Posts

What is the Nvidia Eye Contact AI feature? Learn how to get and use the new Nvidia Broadcast feature. Zoom meetings and streams get easier.

Nvidia Eye Contact AI can be the savior of your online meetings

January 30, 2023
How did ChatGPT passed an MBA exam

How did ChatGPT passed an MBA exam?

January 27, 2023
What is AI prompt engineering? Learn how to write a prompt with examples. ChatGPT prompt engineering and more explained in this article.

AI prompt engineering is the key to limitless worlds

January 27, 2023
What is Analytics as a Service (AaaS): Examples

Transform your data into a competitive advantage with AaaS

January 26, 2023
Google code red: ChatGPT and You.com like AI-powered tools threatening the search engine. Moreover, latest Apple Search rumors increased the danger.

Google code red: ChatGPT, You.com and rumors of Apple Search challenge the dominance of search giant

January 26, 2023
Tome AI offers a new way to create presentations easily

Tome AI offers a new way to create presentations easily

January 25, 2023

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

LATEST ARTICLES

Fostering a culture of innovation through digital maturity

Nvidia Eye Contact AI can be the savior of your online meetings

How did ChatGPT passed an MBA exam?

AI prompt engineering is the key to limitless worlds

Transform your data into a competitive advantage with AaaS

Google code red: ChatGPT, You.com and rumors of Apple Search challenge the dominance of search giant

Dataconomy

COPYRIGHT © DATACONOMY MEDIA GMBH, ALL RIGHTS RESERVED.

  • About
  • Imprint
  • Contact
  • Legal & Privacy
  • Partnership
  • Writers wanted

Follow Us

  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
No Result
View All Result
Subscribe

This website uses cookies. By continuing to use this website you are giving consent to cookies being used. Visit our Privacy Policy.