The rules of Tinder are pretty simple: You swipe right, or you swipe left. 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. Every time you swipe right, Tinder learns a clue about what you look for in a potential match. The more you swipe, the closer Tinder becomes to piecing together the mosaic of your dating preferences. As millions of people spend hours flicking their thumbs across their screens, Tinder's data scientists are carefully watching.
Today, the company puts some of that data to use with a new feature called Super Likeable, which uses machine learning to predict which profiles you're most likely to swipe right on. 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 some people would argue that Super Likes seem a little desperate.)
Super Likeable builds on a machine learning tool called TinVec, which Tinder announced earlier this month at the Machine Learning Conference in San Francisco. The proprietary tool sifts through vast amounts of swiping data to find patterns—like your tendency to dig men with beards—and then searches for new profiles that fit those patterns. Tinder then adds those profiles to your swiping queue. The more you swipe, the sharper the predictions become, and (theoretically, at least) the more likely you are to swipe right on the profiles Tinder expects you will.