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Between dates, they completed a two-minute questionnaire about their feelings toward the person they’d just met. It was easy to predict people who were generally friendly and people who were exceptionally picky.The researchers later compared the algorithm’s predictions to participants’ actual reports of romantic desire. But the machines had zero ability to match a specific person with another person.
"The users have an expectation of how the site is going to work, so you can't make big changes all the time." The biggest problem they face on the tech side, Maxim says, is to make sure everyone can find somebody.
To even this out they look at the number of unread in-box messages and place users further down the match list if he/she has tons of them.
The popularity metric (which isn't displayed on people's profiles) helps them match people with similar status on the site.
In their algorithm that matches users with one another they use match percentages, which basically quantifies how much users have in common, along with their popularity and in-box messages.
On any dating site, he says, a small subset of users will receive the majority of the messages.
Then I found out that I didn’t have to write a profile at all: All I needed to do was fill out some basic personal information. One day, I received an email from the service with a picture of my ideal match. In some cases, machine learning excels at spotting patterns and making predictions.