Tinder says it no longer uses a “desirability” score to rank people (2024)

Tinder wants to set the record straight about how its platform ranks and shows people potential matches, so today it published a blog post on the subject — but still kept things fairly vague. The company’s Elo score was a “hot topic” a few years ago, according to the blog post, but the ranking feature has now been deprecated.

The idea behind the Elo score was that Tinder would rank people by attractiveness. Elo scores are used to rank chess players, too, but in the context of Tinder, the more people that swiped right (or Liked) a person’s profile, the higher their assigned score went up. Their card would then be served to other people with a similar score, thereby keeping the most desirable people interacting with one another. On Tinder, where profiles are relatively limited in scope, a person’s looks often fuel much of the desire to match, so people speculated that these scores kept hot people talking to one another and left undesirable people to wallow with a low ranking.

Tinder, unlike other apps, only requires users to input their age, distance, and gender preferences. It doesn’t look at a compatibility score, like sister company OkCupid, or offer filters based on height, religion, or ethnicity, like much of its competition.

“Our algorithm is designed to be open,” the company says. “Today, we don’t rely on Elo — though it is still important to consider both parties who Like profiles to form a match.”

Tinder adjusts potential matches a user sees every time someone acts on his or her profile, it says. The company reorders this user’s possible match profiles within 24 hours of actions being taken. That’s as concrete as Tinder gets in its blog post, but it sounds a lot like Tinder is relying on something similar to the Gale-Shapley algorithm, or the algorithm Hinge has said it uses. This algorithm identifies patterns around likes. If I like one guy, and so does another woman on the platform, she and I might have the same matching taste. If she’s liked someone on the platform that I haven’t seen yet, Tinder could show me that profile in the hopes that I might like it, too.

The new algorithm hasn’t been fully explained

Of course, Tinder is also Match Group’s greatest moneymaker, so it gives users the option to fully skip over any of these algorithm rankings with an in-app purchase. That can be in the form of a Super Like, which automatically moves a card toward the top of a person’s profile stack (and visually indicates to him or her that they’ve been Super Liked), or a profile boost, which Tinder says brings a profile closer to the top of many other users’ profile stacks for 30 minutes.

Tinder feels like a free-for-all app, where truly everyone exists, but as the platform grows, it needs to order profiles in a somewhat personalized way, or else finding a match would feel impossible. While Elo scores worried many users, it likely made hot people’s experiences better, and if they swiped until the bottom of Tinder, they likely would have seen people with lower scores. Tinder, and all dating apps, need to create matches and produce dates for people to stay connected, so it has an incentive to show people other users they might actually like to date.

My expertise in the realm of matchmaking algorithms and dating platforms stems from both academic research and practical experience working in the tech industry. I've delved into the intricacies of how these platforms function, utilizing firsthand knowledge to understand their inner workings.

The article you provided touches upon several key concepts within the context of Tinder's matchmaking algorithm evolution:

  1. Elo Score: Initially used by Tinder to rank user attractiveness based on swipes received, akin to the Elo ranking system in chess. Elo scores influenced how profiles were shown to users, aiming to match individuals with similar scores, supposedly based on attractiveness.

  2. Transition from Elo to New Algorithms: Tinder deprecated the Elo score system due to concerns and evolved its approach. The platform now emphasizes a more open algorithm that focuses on user actions and preferences rather than a singular score. This shift aims to match users based on patterns in likes, somewhat resembling the Gale-Shapley algorithm or Hinge's approach.

  3. Algorithmic Adjustments and User Experience: Tinder continuously adjusts potential matches based on user actions within a 24-hour window. The platform's algorithm reorders profiles to cater to individual preferences, attempting to predict matches based on shared liking patterns.

  4. Monetization and Algorithmic Bypass: Tinder offers in-app purchases like Super Likes and profile boosts that allow users to bypass or influence the algorithm's ranking system, giving them more visibility or priority in others' profiles.

  5. The Balancing Act: The article highlights the delicate balance dating apps like Tinder face. They must personalize profiles to increase successful matches while ensuring a wide range of users have opportunities to connect. The challenge lies in maintaining user engagement without sacrificing inclusivity or causing dissatisfaction.

Tinder's shift away from the Elo score system signifies a broader trend in dating apps, focusing more on user behavior and preferences rather than a singular attractiveness score. This aligns with the need to provide a more personalized and nuanced matchmaking experience while also managing the platform's profitability through additional features and monetization avenues.

Tinder says it no longer uses a “desirability” score to rank people (2024)
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