That’s straightforward. But the other half of the trick is not: it has to do with analyzing the way customers browse rather than the rankings and feedback they deliver. It’s the difference between recommending a match for SensitiveDude450 because we’re “both eldest children” and recommending a match because the site knows that users like SensitiveDude click on the profiles of women who make a bit less money, are shorter, and share the same religion.
“Each of those companies invests heavily in R&D to try and find ‘cheats’ [that they use as] a competitive advantage,” says Jacobs. “They can’t ever share details, because they consider it a secret sauce. Also, my guess is that these cheats are not along single vectors, although ethnicity would probably be straightforward to identify as something that people would claim not to care about when, of course, they did.”
By “cheats,” Jacobs doesn’t mean that Match’s developers have automated their insights about who tends to like whom. More likely, the programmers use an algebraic tool called singular-value decomposition, or SVD, which has many applications in statistics. Match.com’s computers are ignorant of the qualities that humans are thinking of when they use terms like religion or body type. Instead, they recognize patterns: SVD assigns values to the likelihood that two users with various combinations of stated preferences and characteristics will think each other a good match.
After Jacobs had filled me in on LSI, it made sense that the explanations Match gave me (“You share a birth month!”) were simplifications. It generated them after it found a match by observing whose profiles I spent the most time reading and whose profiles other users like me have liked, among any number of other factors.
It’s creepy, the idea that a computer can suss out what it is that SensitiveDude really wants–or at least, what he would be looking for if he existed. The only thing that makes it less creepy is that, at least in Ruby’s case, all that predictive technology turned out–over and over again–to be wrong.
More time spent on the site might have paid dividends for Ruby: the site would have gotten to know her better. Lately, though, she has been searching the offline world for matches. This approach has its upside. For starters, you can wait until after you’ve actually met someone to show him what you look like in a bikini.
Emily Gould is a former editor at Gawker.com.