I’m REALLY happy to talk about this subject for at least two good reasons.
Firstly, AI and machine learning applied to Tinder is definitely a spicy conversation topic.
Believe me, it will be a fascinating journey to discover the artificial intelligence algorithms that make Tinder so damn effective.
Secondly, telling my wife that I’m inquiring about Tinder in the name of science is priceless!
AI is everywhere, also in Tinder!
Unfortunately, the development team behind Tinder isn’t particularly inclined to reveal the secrets of its algorithms. So the technical information available to us is quite limited.
Anyway, it’s still enough to get a general idea of how AI, and more specifically machine learning, are used by this app.
After a little research, I found three main fields of application:
Tinder implements a machine learning-based algorithm to generate personalized recommendations.
The app uses ML to automatically screen for potentially offensive messages.
“Smart Photos” feature
Machine learning helps users identify which profile pics are the most successful.
Before delving into these topics, let’s introduce the two protagonists of our research: Tinder and machine learning. It shouldn’t be a surprise for you. It was all written in the title.
What is Tinder?
Tinder is an online dating application to find the great love of your life, get married, and have children have fun.
The mechanism is pretty straightforward. Users create a personal profile with pics, interests, and a small bio.
Then, they can check other profiles and swipe to like or dislike them. Once two users have “matched” with a mutual like, they can text each other.
Considering that every sentient being in the universe already knows about Tinder, we can probably move on.
What about Machine learning?
If you came here for that “machine learning” in the intro, probably you already know what we are talking about. So, I’ll make it short.
Machine learning (ML) is a branch of artificial intelligence. It focuses on the creation of computer algorithms which can improve their performance automatically through experience.
ML algorithms are able to recognize specific patterns in sets of data, build mathematical models to represent them, and use these models to make predictions or decisions without being explicitly programmed.
A Belarusian colleague once told me that ML is essentially magic. If this Harry Potter-style explanation is not enough for you, take a look at my two articles about machine learning and its main categories.
I wrote them with the same love with which my grandma cooked Italian tortellini.
“ML algorithms are able to recognize specific patterns in sets of data, build mathematical models to represent them, and use these models to make predictions or decisions without being explicitly programmed.”