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In our previous posts about Netflix personalization, we highlighted the importance of using both data and algorithms to create the best possible experience for Netflix members. We also talked about the importance of enriching the interaction and engaging the user with the recommendation system. Today we're exploring another important piece of the puzzle: how to create a software architecture that can deliver this experience and support rapid innovation. Coming up with a software architecture that handles large volumes of existing data, is responsive to user interactions, and makes it easy to experiment with new recommendation approaches is not a trivial task. In this post we will describe how we address some of these challenges at Netflix.
A major contribution in the OTT architecture information pool.