Online recommendations systems are usually based on the assumption that if you’re interested in one thing, you’ll be interested in similar things–a principle called “relevancy.” That might work on shopping sites, like Amazon, but it turns out, according to a company called Outbrain, that it doesn’t work so well for content sites.
The New York-based company is turning the whole publishing recommendation engine business on its head. Unlike many such engines, Outbrain doesn’t serve up links based on how similar the topics of the new stories are to the one the reader is already reading.
It also eschews another standby in the business: serving up links based on the likelihood that a reader will click on them. Instead, the system makes its determinations based on how likely the reader is to stay engaged on the site once they click through.
It’s an idea that’s gaining traction. Outbrain’s recommendation engine is now used on almost 1,000 brand publisher sites in the U.S. and Europe (as well as tens of thousands of smaller sites). And today, the company is announcing that it has closed a $35 million round of funding that brings its total financing to $64 million.
The company’s chief insight has been that traditional recommendation strategies don’t actually deliver the results publishers are looking for. Publishers don’t just want readers to click on links. They want readers to stick around on the site, surfing from one story to another–and, of course, running their eyes across a bevy of ads in the process.
Serving up links based on relevance actually works very poorly, Outbrain founder and CEO Yaron Galai tells Fast Company. Unless the reader is a sports fan who wants to read obsessively about their home team, related links don’t actually perform as well as links on seemingly unrelated subjects, but ones that Outbrain’s algorithms have discovered somehow appeal to similar readers.
