Tinder and ML: the Recommendation system
After the previous theoretical premise, it’s time to analyze the ways in which AI and ML contribute to Tinder’s mysterious functioning.
Firstly, Tinder uses VecTec, a machine-learning algorithm paired with artificial intelligence to generate personalized recommendations.
According to Tinder chief scientist Steve Liu, Tinder users are defined as Swipes and Swipers.
Each swipe made is mapped on an embedded vector that represents the possible characteristics of the user. For example, hobbies, education, interests, professional career…
When the system recognizes a similarity between two built-in vectors (which means that users share common traits) it will recommend them to each other.
This process allows Tinder’s algorithms to progressively improve their performance in identifying users who could co-swipe and match.
Do we speak the same language? It’s a match!
An additional ML-based algorithm that contributes to successful matchings is Word2Vec.
Let’s say that this algorithm is Tinder’s personal linguist.
In fact, while TinVec analyses the users’ characteristics and learns from large amounts of co-swipes, Word2Vec focuses on words. Specifically, it identifies the communication style of users (slangs, dialects, choice of words based on the context…).
Taking into account this data, similar swipes are grouped together in clusters and the users’ preferences are represented through the embedded vectors that we already mentioned.
Users with similar preference vectors will be mutually recommended by the system more often.
Pros and cons
As we have seen, Tinder learns the kind of profiles you like and dislike based on your previous swipes left or right, in combination with these profiles’ characteristics.
This allows the app to customize its recommendations for you.
On the other hand, we should be aware of some unpleasant implications when we rely on algorithms to make a selection of people based on their attributes.
The risk is to promote a system of classification and clustering which tends to keep away less desirable profiles from the “alpha” ones.
An example? Back in 2014, the famous dating website OkCupid published a study about racial bias in users’ preferences and behavior.
Based on that research, the discrimination of black women and Asian men seems to be pretty common in online dating environments.
Pretty much what happens to nerdy guys every day. But that’s another story.
Machine learning vs Harassment
Tinder relies on machine learning to automatically scan potentially offensive messages and evaluate if some user is a bit too much flirty or just a maniac.
When the system detects a controversial message, Tinder asks the receiving user a confirmation of the potential offense and direct him/her to its report form.
As you can imagine, this mechanism can falter in many borderline situations.
First of all, sensibility towards a certain type of communication changes radically from person to person.
Second, a flirty language perceived as vulgar in many situations can be perfectly tolerated or even appreciated in a dating context.
The algorithm knows if you are a bad boy… or a bad girl.