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New York Times Turns to Machine Learning for Better Understanding of Readers

By   /  June 23, 2014  /  No Comments

Matthew Ingram of GigaOM recently wrote, “You might not think an applied mathematician who does research in biology and has a PhD in theoretical physics would have much to offer a 163-year-old newspaper publisher.”

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Matthew Ingram of GigaOM recently wrote, “You might not think an applied mathematician who does research in biology and has a PhD in theoretical physics would have much to offer a 163-year-old newspaper publisher, but Chris Wiggins, head of the data science team at the New York Times, told attendees at the Structure conference in San Francisco that machine learning can do much the same thing for media companies as it does for research biologists: namely, make sense of a whole pile of data.”

 

Ingram goes on, “In the case of the Times, that data is about things like what pages readers look at, how long they spend reading them, what they click on or read before and afterwards — and especially how that behavior relates to the paper’s advertising and the reader’s desire to sign up for or renew a subscription. And with the recent launch of two new mobile products, including the NYTNow app, the paper will have even more data on user behavior that it can play with and comb through for insights, Wiggins said.”

 

Read more here.

 

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