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Dating matchmaking algorithm

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How do they decide who matches up with who? Sometimes, the process is very simple. Each profile has a list of attributes or interests that members check off. Some sites, like match. Each matching attribute is assigned a different weight depending on how important it is to the user.
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How Online Dating Works

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Secret of eHarmony algorithm is revealed

A front-row seat in a crash course on app-based dating was the perfect place for JoAnn Thissen. Online dating takes a lot of nerve, and the year-old retired marine geologist was working up her courage. There were men and women, millennials and baby boomers, singles and people in relationships. Peak dating season approaches with the holidays, and the love lives of tens of thousands of Chicagoans hinge on how algorithms behind popular dating apps like Tinder, Hinge and Match piece together their data. Even a decade ago, 1 in 3 marriages started online, one study suggested, and dependence on dating apps has only increased. Some users fret over creating the perfect profile to rope in the ideal mate.
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The algorithm method: how internet dating became everyone's route to a perfect love match

Back in , I decided to try online dating. My biggest concern was about how to write my dating profile. I also struggled with opening up with strangers, and I thought this trait would hamper my ability to find the woman of my dreams. All I needed to do was fill out some basic personal information. The machine matchmakers would do the rest.
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It meant a lot of late nights as he ran complex calculations through a powerful supercomputer in the early hours of the morning, when computing time was cheap. While his work hummed away, he whiled away time on online dating sites, but he didn't have a lot of luck — until one night, when he noted a connection between the two activities. One of his favourite sites, OkCupid , sorted people into matches using the answers to thousands of questions posed by other users on the site. McKinlay started by creating fake profiles on OkCupid, and writing programs to answer questions that had also been answered by compatible users — the only way to see their answers, and thus work out how the system matched users. He managed to reduce some 20, other users to just seven groups, and figured he was closest to two of them.

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