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In fact, other dating apps like Tinder boast much larger user bases, and therefore much more data for an algorithm to absorb. In the near term, is machine learning truly a sustainable competitive advantage for Hinge? It is not yet clear whether Hinge is the best-positioned dating app to win with AI-enhanced algorithms.
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Outstanding questions as Hinge looks ahead
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“Longer term, could help to establish Hinge as place that’s for people who want relationships, not just serial dates or hookups.” Hinge’s ‘We Met’ feature (source: ) Recommendations and actions “‘ We Met’ is actually focused on quantifying real world dating successes in Hinge, not in-app engagement,” writes an analyst from TechCrunch. In addition to allowing Hinge to better track its matchmaking success, it can also use this data as feedback to teach its matching algorithms what truly predicts successful matches offline over time. This was a simple, but powerfully important, step for Hinge. In 2018, Hinge launched another feature called ‘We Met,’ in which matched users are prompted to answer a brief private survey on whether the pair actually met up offline, and what the quality of the offline connection was. Hinge’s ‘Most Compatible’ feature (source: ) Hinge creates valuable teaching data using ‘We Met’ In this way, machine learning is helping Hinge solve the complex problem of which profile to display most prominently when a user opens the app. Using this revealed preference data, the algorithm then determines in an iterative fashion which pairings of users would lead to the highest-quality ‘stable’ matches.
Īt Hinge, the ‘Most Compatible’ model looks at a user’s past behavior on the platform to guess with which profiles he or she would be most likely to interact. Gale-Shapley is most famously 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. The Hinge CEO shared that this feature was inspired by the classic Gale-Shapley matching algorithm, also known as the stable marriage algorithm.
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Pathways to Just Digital Future Watch this tech inequality series featuring scholars, practitioners, & activists Hinge’s first public foray into machine learning was its “Most Compatible” feature, launched 2017. 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. “3 out of 4 first dates from Hinge lead to seconds dates,” touts their website. 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.
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This model is not a massive departure from the formulas used by older competitors like OkCupid and Tinder. 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. Its basic premise is to show a user some number of profiles for other suitable singles. adult population using dating apps or websites has grown from 3% in 2008 to over 15% today. With the rapid rise of, Tinder, Bumble, and more, it is unsurprising that recent estimates suggest that the proportion of the U.S. “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.