In addition to the homepage, detail information for each program is also tailor for users:
Which version of the trailer and highlights should be shown?
Which version of the poster
(Netflix calls it “artwork”) should be shown to attract clicks?
Netflix it 10 versions of highlights for House of Cards to suit the interests of different audience groups. For Stranger Things alone, there are 9 different potential artworks. Each version of the artwork highlights something unique about the movie. It can be a close-up of a famous actor from the show, a classic scene, or a scene that best represents the movie. The selection of artwork is bas on past user behavior and interests. For example, if you have watch a lot of action/adventure movies in the past, Netflix will use artwork that shows action or exciting scenes when introducing you to new shows.
Collect user behavior data in real time
Hyper-personalization is impossible without a large amount of real-time data. Netflix’s recommendation algorithm continuously updates and modifies video recommendations bas on different user data sets, including:
Video content that users click on and search for
User interactions with the video, including where bc data america they paus , rewatch , or left off, and how long they watch .
User viewing habits, including date, time and device type
Video content that users did not click on (i.e. videos that users were not interest in)
Similar audience behavior data
Every move a user makes on the platform, no matter how much time do you have in your business to think how seemingly insignificant, is record and analyz by Netflix. In addition, recent user behavior is given more weight in the calculation process.
When you click on a video, you can see a match percentage score below the Play button. This is Netflix’s pr iction of how well the video matches your interests.
Each user receives a unique percentage score for the uk data same video. This is because users have different interests and viewing habits. A video might be a “91% match” for user A, but only a “71% match” for user B.
Improving system accuracy by encouraging user fe back