One of the core potential benefits of recommendation systems is their ability to continuously calibrate to the preferences of the user. This makes products that become more and more “sticky” in their customer retention as time goes on: You’re much less likely to switch to a Netflix competitor when Netflix has such a wonderful sense of which movies and shows you might want to watch next (i.e. they “know you so well”). Because most of Netflix’s revenues come from a fixed-rate recurring billing model subscription, the company’s biggest ROI “win” with recommendation systems is retention.
Target users: Existing and new users of Netflix
Goals: Users want to watch new movies and tv shows based on their ‘taste’ without doing lots of searching in the Netflix website or on the internet.
How to reach the goal: Using personalized recommendation service.
Improvement for recommendation engine: Currently recommendation engine works for the household. Even though the service allows to have user profile, the recommendations user see are for the household. This I feel is counterproductive. The recommendation engine should work for each profile and give movie suggestion based on each profile owner’s taste.
https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429