Two years ago, Nancy Kaup was a 31-year-old single mother who was frustrated with dating. She had spent six months on the website eHarmony, filled out a 400-question survey about herself, and begun receiving daily “matches”—profiles of men whom the site deemed compatible. But none of them worked out. She decided not to renew her subscription. Two days before her profile expired, however, a man named Jon Anthony signed up for the service.
Nancy showed up in Jon’s first round of suggested matches, and he contacted her. “He was my last match and I was his first,” she says. Their first date was at a wine tasting in Albuquerque, New Mexico, where they both live. Although it lasted only an hour or two, the next day Nancy told her friends at work that she had met her future husband. “I knew right away,” she says. “It’s weird, because I’m not usually like that.”
The online dating industry is bigger than ever. An estimated 40 million Americans are members of dating services offered over the Web or on mobile devices, and in China the number has grown to 140 million people. But matching up millions of members is a major technological challenge as well as an emotional one. While some sites simply let users browse for dates, many now offer some kind of system, if only to make suggestions. And companies in this competitive market are in hot pursuit of ways to make those suggestions more sophisticated and personalized. To do that, they are deploying machine-learning algorithms that are adapted from completely different types of online shopping.
Joseph Essas, vice president of technology for eHarmony, was lured to the company from Yahoo three years ago. Since then, he has developed and implemented a new layer of predictive matching algorithms that are based on Yahoo’s system for targeting advertising to specific users who have revealed preferences and behaviors over time. The matchmaking software gathers 600 data points for each user, including how often they log in, who they search for, and what characteristics are shared by the people they actually contact.
According to Essas, eHarmony has used this information to predict how likely it is that two people will engage in conversation, which helps determine which matches will be suggested on any given day. “How do we get people talking to each other to recognize their commonalities?” he asks. The new software, he says, gets more such conversations started, “with 34 percent more back-and-forth communication compared to a year ago.”
While most of these new techniques were installed after Nancy first met Jon, eHarmony has built stories like theirs into their model, as these are the kind of matchups the company aims for. Jon and Nancy were engaged within two months, and in five more months they were married. Now they have a baby on the way.