When a user signs up to eHarmony they fill out a lengthy questionnaire about the type of person they are, their likes and dislikes, beliefs, values and preferences in potential partners.
The information is fed into the company’s closely guarded, secret algorithm, which serves up the most compatible matches in its user base.
The matching algorithm is based on data collected from interview with more than 50,000 married couples in 23 different countries, from which the company has derived a mathematical model of a successful relationship it says.
Cue butterflies? Not particularly, explains eHarmony VP of technology Prateek Jain.
“So, I found you the most compatible person on the planet. What if you are not attracted to them?” he says.
It’s what happens next that counts. Over the last few years eharmony has been leveraging machine learning models and distribution algorithms to boost the butterflies, and help hundreds of users find true love every day.
The result is the ultimate recommendations service for singles, the company says, which leads to an average 438 people getting married through the site every day.
“We say we’re like Netflix,” Jain explains, “but the movie has to like you back.”
Algo’ my loving
In the time before Tinder, telling people you had met your partner online was met with more than a little derision.
EHarmony, founded by clinical psychologist and Christian theologian Dr Neil Warren and his son-in-law, was launched in 2000, the world’s first algorithm-based dating site.
“I think back in the day when we started I think eHarmony was primarily focused on the compatibility part of its matching algorithms, which was the secret sauce what made it popular,” Jain, who joined the company in 2011 explains.
Despite doubt about the algorithm’s success rate versus other methods – earlier this year eHarmony ads in the UK claiming its system was “scientifically proven” were banned – it certainly works for many.
By 2012, the company had a 14 per cent share of the $2-billion-a-year US dating services industry, according to research firm IBISWorld, boasting 750,000 paid subscribers and 10 million active users.
The company has a number of regional sites, plus same-sex relationship brand Compatible Partners.
Australia is eHarmony's second-largest market by profitability and accounts for about nine per cent of global business revenues.
“One thing that became apparent to us was that compatibility was working and doing its job. But we were not seeing a lot of communication happening between the members. We could find you the most compatible person on the planet but if you’re not attracted to them, if you’re not going to reach out to them with a message or call then that match is not going to be success,” he said.
A few years ago, eHarmony started experimenting and investing in big data and machine learning.
It has since added extra layers to its compatibility system, with around 20 ‘Affinity’ models at work to ensure the sites recommendations are more personalised and primed for users. Now, matches are based on far more than just the questionnaire; such as how users behave on the site, the profiles they click on and the content of their self-descriptions.
“All these indirect signals we look for, it allows us to refine the filter,” Jain says.
The looks, of love
From the outset, Jain says, eHarmony’s founders subscribed to the idea that “compatibility shouldn’t be about looks it should be about personal level compatibility”.
Nevertheless, the site’s machine learning models quickly gain an understanding of what you find attractive based on the profiles users interact with.
“We do not ask any direct questions which ask you to define your attractiveness, but based on what are the kind of matches you are reaching out to we can learn who you find attractive as well as where you rate on the attractiveness score based on how people are reaching out to you,” Jain says.
Using Google’s Cloud Vision API, user profile pictures are scanned for a number of features – including hair and eye colour, whether the image shows a beard or moustache as well as ‘has cleavage’ and ‘deduced BMI’.