How The New York Times Is Incorporating Social & Algorithmic Recommendations

The New York Times released Thursday a finished version of the Recommendations platform it quietly introduced in beta in late January.

Available at nytimes.com/recommendations and on the “Recommended For You” tab on article pages, the tool is designed to help logged-in readers “see through the news fog,” as NYT lead technology reporter Nick Bilton put it. It serves up recommended stories based upon the kinds of articles visitors have read.

“We wanted to make the site more engaging, to expose content to our readers on a more customized, personalized basis — and not customized in the way you select your topics like a My Yahoo or iGoogle, but more of a passive personalization,” Marc Frons, CTO of The New York Times, explains. “We created an algorithm that exposes users to content they may not have seen otherwise,” he adds.

The algorithm is one of the most sophisticated we’ve seen on a news site. When serving up recommendations, it calculates a number of factors, including recency (visitors who tend to gravitate toward breaking news should see recommendations for more timely topics), sections, topics and keywords.

A New Model for Curation

Recommendations is part of a broader exploration of new curation and aggregation methods for Times readers. For time immemorial, the editors of the Times have determined what appears on the cover of the paper and, beginning in 2006, what appears on the front page of nytimes.com.

Now, the front page of nytimes.com shows recommendations from one’s Facebook network alongside stories chosen by Times editors. Visitors can easily navigate to the “Most Popular” tab to surface stories that have proven most popular among bloggers and readers.

In the future, we wouldn’t be surprised to see the front-page content of nytimes.com divided into three sections: one for stories recommended by human editors, another with stories recommended by one’s social network and a third that delivers stories chosen by the site’s internal recommendations engine.

“The challenge is to balance recommendations that are editorially driven, based on what editors think is important, with recommendations from the social sphere and algorithmic recommendations, based on what you’ve read and who you are, your own likes and dislikes,” Frons says. “It’s something we’re constantly looking at and experimenting with.”

It’s a tough challenge, especially in light of the Times‘s emphasis — and subsequent reputation — for editor-driven curation. So far, the Times has proven to be open-minded and progressive without overwhelming readers, for which we commend them.

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