The (Data) Science of Online Dating: How Algorithms Are Changing the Dating Game

Terence Shin, MSc, MBA
4 min readJan 25, 2023
Photo by Mika Baumeister on Unsplash

Swipe Right, Match, and Date.

Finding love has never been easier in the world of online dating and data science is playing a bigger role than ever before. Online dating apps have revolutionized the way we connect with potential partners by using sophisticated algorithms to help match users with compatible partners. But how exactly do these dating algorithms work, and are they really changing the game when it comes to finding love?

The Goal of Online Dating

The first thing that you have to understand is that every algorithm is built to solve a particular problem that it was designed to solve, and dating algorithms are no exception.

At the heart of any dating algorithm is the goal of creating matches between users that are as compatible as possible.

Now we can go on for days discussing how “compatibility” is specifically defined. Are two users compatible if they match? Or if they engage in a conversation after matching? Let’s assume the former for simplicity.

The Most Important Factors that Dating Algorithms Consider

The goal of creating matching between compatible users involves analyzing data from a user’s profile, such as their age, interests, and location, as well as their behavior on the app or website. For example, the algorithm may track how often a user swipes right on potential partners, and which types of profiles they tend to match with.

One of the most important factors that dating algorithms consider is similarity. Studies have shown that people tend to be attracted to partners who are similar to themselves, so dating apps use various techniques to identify similarities between users. This can include analyzing users’ interests, hobbies, and even the way they use language in their profiles.

Another important aspect of dating algorithms is predicting compatibility. Some apps use machine learning techniques to analyze patterns in users’ behavior and predict which pairs of users are most likely to hit it off. For example, an…