But in the first batch of suggested dates, the matches seemed to have less in common with the Dude than with each other. That set included a short 23-year-old Jewish woman with a cute photo of herself at a fancy restaurant and two others of herself on the arms of guys. She liked the Economist but also Us Weekly. Her favorite things included “brunch.” The site said we had been matched because we were both dog lovers, eldest children, and athletic and toned.
The second match, too, the site said, was deemed compatible on the basis of birth order and pet preferences. But I noted that she was also Jewish, also young (24), and also short, at five foot two. “Seats at a Yank’s [sic] game are always a winner with me,” her “About Me” section declared. This assertion was reinforced by a photo of her in a jersey at a baseball game. Another photo showed her posing in panties and a tank top.
I clicked “maybe” when the site asked me to say whether I was interested in her, and then I clicked “maybe” on a couple of the other short young Jews–not wanting to click “yes,” which would have automatically informed the women of my interest. But one woman who’d been shown my profile in her top 5 did click “yes,” so I checked her out.
She was a 24-year-old lab tech at a fertility clinic, with an incoherent, heavily misspelled profile. She loved malls and hated country music, and her profile photograph was an odd shot of her sucking on a straw. The site, seeming desperate to find something we had in common, pointed out, “Like you, she’s never been married!” I looked at my own profile to remind myself that I was no prize, but then I shut my laptop. I was beginning to understand the basis of the distrust I’d felt when Ruby joined Match. It was gross to know that actual men sat there as I’d just done, flipping through photos of women so desperate for their attention that they posted photos of themselves in bathing suits, twisting around to accentuate their butts while delivering soft-porn smiles.
All this is big business. Online dating, according to Forrester Research, produced $957 million in revenue in 2008–making it the third-largest generator of online paid-content revenue, after music downloading and gaming–and is expected to grow another 10 percent annually through 2013. Even (or especially) in the face of economic contraction, Match.com is thriving.
As a man on Match, I had the sense that what I was doing was a kind of online shopping, which makes sense. The site uses the same type of data-mining technique, called latent semantic indexing (LSI), that search engines like Google use to rank the relevance of Web pages.
The trick behind successfully matching people and products–or people and other people, or people and other people who’ve packaged themselves into something like products by means of “profiles”–is math. “You and I don’t imagine four-dimensional spaces, but mathematics and computers can,” says David Jacobs, a vice president at the blogging-platform company SixApart, who’s worked with similar technology in designing social-media sites. “Each additional attribute considered creates an extra dimension in the ‘space’ with which Match.com is looking for matches. The algorithm creates a virtual graph which approximates hundreds or thousands of axes.”