The New York Times is looking to machine learning to help it understand reader behavior

Chris Wiggins, a theoretical physicist and mathematician who is the chief data scientist for the New York Times, says he is trying to help the paper detect patterns in the data about user behavior so that it can make better decisions about the product.


One of the things that machine learning and data analysis can do, he said, is search through large datasets about user behavior in order to detect patterns that might indicate which direction the editorial or business side should take a particular product, whether it’s a web offering or a mobile app. Instead of deciding which features need to be focused on in advance, Wiggins added, “we can rely on the data to reveal which features we should be paying attention to,” because of the correlations in those patterns

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