Netflix uses data for a lot more than just recommendations

Netflix is famous for the way it uses algorithms to determine what programs or movies its members might want to watch, but data plays a much broader role inside the company’s streaming service than just informing recommendations. In a blog post on Wednesday, the company explained how it analyzes data to do everything from optimizing playback quality to identifying poorly translated subtitles.
The post, written by Netflix ‎director of streaming science and algorithms Nirmal Govind, highlights several areas in which better algorithms could improve the Netflix experience, focusing largely on how to ensure the best-possible playback in any given situation — or, at least, how to ensure users are getting the playback quality they expect. It might be easy enough to find the right theoretical tradeoff between bit rate and rebuffer rates on streaming videos, or to figure out where (geographically) to place which content on the Open Connect content-delivery network, but nothing is that simple in practice.
“[W]e need to determine a mapping function that can quantify and predict how changes in [quality of experience] metrics affect user behavior,” Govind wrote.

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