A new algorithm proposed the University of Texas at Dallas could help dating app users find the perfect mate. The study was conducted by Dr. Ignacio Rios, assistant professor of operations management in the Naveen Jindal School of Management.
“One of the biggest issues is how to decide which profiles to show to each user in order to ensure that they will get meaningful matches,” Rios said. “In many dating apps, we see a lot of frustrated users because they struggle to find a match that leads to a longer-term relationship. This is partly because of inefficiencies in how these apps work.”
The $12 billion online dating industry includes hundreds of services. In the past two decades, online dating platforms have become one of the most common channels for couples to meet. Previous research found that nearly 40% of couples who met in the U.S. in 2017 did so online.
Rios and his colleagues developed a model that incorporates a novel component: users’ experiences.
Using the industry partner’s data, the researchers studied users’ preferences, such as age, religion and race, and behaviour, such as whether each user logged in, and, if so, how they evaluated the profiles shown to them.
The study found that the more matches a person has had in the recent past, the fewer likes they give to other profiles. This suggests a history effect, Rios said.
Estimates show that each additional match reduced the probability of a new like by at least 3%.
“We observed that users are less likely to like other profiles when they have recently succeeded in obtaining more matches,” he said. “This might be because users keep in mind the amount of time and energy they can spend in the app, and thus if they had many matches in the recent past, they expect to spend their time on those matches instead of liking other profiles.
“Another likely reason is that users update their beliefs about their own attractiveness, and thus become pickier. Finally, a third possible reason is that users have faith that their new matches will work out, so they avoid liking new profiles.”
The researchers incorporated these findings into a new algorithm to solve the platform’s problem. Rios said the algorithm considers the probability that both sides will like each other and prioritises the users who have not obtained matches in the recent past, with the assumption that they will be more likely to like the profiles shown to them.
Using simulations on real data, the researchers found that the proposed algorithm improved the overall match rate between 20% and 45% relative to the industry partner’s current algorithm. Those results persuaded the company to test the algorithm in practice.
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