NetFlix TV

How Netflix’s Recommendation System Functions?

Ever wondered how Netflix suggests viewing some show or movie and when you watch it, it turns out to be great? Netflix suggestions engine is based on a black box of an algorithm. The algorithm is fed various kinds of data. Though the suggestions engine is not what is talked about a lot, it is a very important part of the content delivery system. Netflix starts to monitor your content viewing habits since Netflix Com Activate screen has been passed. Hence, the type of information which you supply plays a key role in creating a content based search more optimised.

Various kinds of data used

Netflix contains majorly 2 kinds of data from the users. Once the has been done after activation, users already have some choices to view content about. This kind of data is known as explicit data. this data is collected by Netflix Tv on the basis that users are providing it to the app by themselves. Though users do not know that this data can be used for suggestions, this is actually used for the same. Hence the explicit data collected by Netflix plays a vital role in search results which are shared by Netflix to users.

But that is not it. What if users are watching crime based content till now but are willing to switch to horror or comedy now. How would that be shown in suggestions? It means that the suggestions engine has failed. Certainly not, as explained at  to a user’s query. Whatever we watch, has some kind of pattern. It solely does not depend upon the explicit data which is provided by the users. There is some other kind of data which is also provided by users and it is used to provide suggestions.

According to  the data such as time taken to complete a TV show paused moments, skipped parts, stopped shows, as well as biased based on the actors etc, is used. It means that if you have watched the Batman trilogy, not only will you get suggestions based on the superhero genre, but also some of the comedy movies done by Christian Bale. Another kind of information includes but is not limited to the upvotes given to the videos, the languages in which a content is seen etc. Suggestions engine also keeps on changing its suggestions based on the time of the day when users have watched the content.

This kind of information supply is known as implicit information. This is the information that users supply which they are unaware of. The suggestions engine is fed all the explicit and implicit information and suggestions are churned out. But it also has a specific process. Netflix has a user base which is called the taggers. A user can sign up at  as a beta tagger. These beta taggers have a job to view all the content on Netflix without skipping any part. Then they tag specific parts of a single show of a single episode with tags such as funny, adult, Charlie Sheen etc.

Basically, taggers fill in the metadata in a TV show or a movie. Netflix starts monitoring your viewing habits since you pass the www Netflix Com Activate page. Hence, the type of viewing you do, such as repeating movie scenes is fed to Netflix and hence it suggests content to the users.

There is also a weighting given to the content viewership timestamp. It means that the most recently viewed content is given more weight than the content which was viewed a month ago and so on.

Hence, this is how information supplied by users is used to provide suggestions based on the viewership.