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You like someone s profile (right), or you don t (left). Occasionally, you might send a Super Like—the digital version of showing up at someone s doorstep, bouquet of flowers in hand, blasting Kiss Me by Sixpence None the Richer out of a boombox—but otherwise, there s not much nuance. The Tinderverse exists in black and white. But those simple decisions translate into a lot of data. As millions of people spend hours flicking their thumbs across their screens, Tinder s data scientists are carefully watching. Those profiles will pop up periodically in groups of four, and users will be able to send one of them a bonus Super Like. (Yes, you have to send a Super Like. Tinder claims that doing so increases your likelihood of matching by three times, though would argue that Super Likes seem a little desperate.

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)Sorry, But if You re Married, Browsing Tinder Totally Makes You a SnakeSuper Likeable builds on a machine learning tool called TinVec, which Tinder announced earlier this month at the Machine Learning Conference in San Francisco. “The beauty of AI is that it incorporates all of those inputs into its ranking system. ”Tinder already uses machine learning to fine-tune other aspects of the matchmaking process. It also developed to surface things in common, like a shared hometown or a mutual interest in videogames.

Tinder s greatest asset in developing these kinds of algorithms may be the overwhelming amount of data the app collects from its massive user base. There are roughly 76 million matches on Tinder every day. That adds up to over 75 billion matches made since Tinder launched five years ago. Using all that information on who likes who, Tinder says its TinVec algorithms can accurately predict who you ll like next with shocking accuracy.

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The idea behind Super Likeable is to surface these profiles faster. Whether you do or not is a way for Tinder to check if it’s getting the equation right, and then adjust its algorithms accordingly. For now, Tinder’s only rolling out Super Likeable to users in Los Angeles and New York. When a Super Likeable box does pop up, it ll always offer four profiles and one Super Like.

In some ways, the feature seems to further reduce the matching process to criteria on a checklist, resurfacing the same types that people already know they like: men with beards, or women who wear glasses. Algorithms are good at finding the profiles that include photos of beards or glasses, and not so good at determining human chemistry. Norgard says it s not quite so simple.

“Sometimes people may think they want one thing, but then when they see something totally different that interests them, it helps them realize that their original filtering criteria might not have been entirely accurate, ” he says. Use of this site constitutes acceptance of our (effective 8/76/67) and (effective 8/76/67). Affiliate link policy. Your California privacy rights.

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