latent Dirichlet allocation

Archive of posts with the tag
latent Dirichlet allocation

Ideas | Mar 28, 2016

A quick look at recommendation engines and how the New York Times makes recommendations

A recent prediction that algorithmic curation would be one of the major trends of 2016 got me thinking about news recommendation engines. I’ve always been curious about the technology so I recently started digging into what makes them work and realized there is a whole lot to learn. But a little research and conversation with a newsroom technologist at New York Times helped me to understand how they work. First you should know that the...

NICAR | Mar 10, 2015

NICAR 2015: Machine learning lessons for journalists

by Anushka Patil

Machine learning is certainly not a new concept in journalism, but it seemed to enjoy plenty of prominence at NICAR this year — fantastic news for newbies to the field like me. I attended several sessions on it, both theoretical and technical, and a few key concepts came up repeatedly. Whether this year’s conference was your first exposure to machine learning, or you’re a seasoned pro, here are four takeaways worth reviewing: Machine learning is...

Archive of posts with the tag latent Dirichlet allocation

A quick look at recommendation engines and how the New York Times makes recommendations

NICAR 2015: Machine learning lessons for journalists

Archive of posts with the tag
latent Dirichlet allocation