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Hinge and Machine Learning: The makings of a perfect match

Hinge and Machine Learning: The makings of a perfect match

“There are plenty of fish in the sea…” To a modern dater, this old adage about finding love seems almost eerie in its prescience of the emergence of online dating. With the rapid rise of Match, Tinder, Bumble, and more, it is unsurprising that recent estimates suggest that the proportion of the U.S. adult population using dating apps or websites has grown from 3% in 2008 to over 15% today .

One such app, Hinge, launched in 2012. Its basic premise is to show a user some number of profiles for other suitable singles. If a Hinge user spots someone of interest while browsing, he or she can reply to a particular element of that person’s profile to start a conversation – much in the same way a user on Facebook can “like” and comment on another user’s newsfeed posts.

This model is not a massive departure from the formulas used by older competitors like OkCupid and Tinder. However, Hinge differentiates itself with the pitch that it is the best of all the platforms in creating online matches that translate to quality relationships offline. “3 out of 4 first dates from Hinge lead to seconds dates,” touts their website .

One way that Hinge purports to offer better matches is by deploying AI and machine learning techniques to continuously optimize its algorithms that show users the highest-potential profiles.

Pathways to Just Digital Future

The Hinge CEO shared that this feature was inspired by the classic Gale-Shapley matching algorithm, also known as the stable ously used for matching medical residents to hospitals by assessing which set of pairings would lead to ‘stability’ – i.e., which configuration would lead to no resident/hospital pair willingly switching from the optimal partners they are each assigned .