News & Events

UI Team's Dating Algorithm Could Mean Better Matches

University of Iowa researchers have uncovered a curious pattern in human behavior when it comes to online dating services: People think they know what type of person they want to date, but their behavior online says otherwise.

So, how do you find the perfect match?

Kang Zhao, assistant professor of management sciences at UI's Tippie College of Business, and UI doctoral student Xi Wang have created an algorithm for dating sites that uses a person's contact history to recommend potential partners.

"Your actions speak louder than your profile," Zhao said. "Your profile might say you like tall women, but all the women you contact are short."

The algorithm takes that disconnect into account by combining two factors: a person's taste (what type of person they approach) and a person's attractiveness/unattractiveness (how many of those people reciprocate that contact).

"You have to like someone to approach them, and there must be some sort of attraction for them to respond," Zhao said. "A person's unattractiveness is reflected by those you approached but didn't get back to you. Maybe they were out of your league.

"We can recommend potential partners in the same league with you," he said.

So far, Zhao has been approached by three different online dating services interested in using his algorithm. He declined to name those services.

During their research, Zhao and Wang used data provided by a popular commercial online dating company whose identity is being kept confidential. They looked at 475,000 initial contacts involving 47,000 users in two U.S. cities over a 196-day span. The users included 28,000 men and 19,000 women. Men made 80 percent of the initial contacts.

Zhao said that according to his data, only 25 percent of those initial contacts were reciprocated. But when the algorithm is applied, those return rates jump to 44 percent.

"Your previous actions are a better reflection of your tastes," Zhao said.

Wang said he used online dating services in the past but was always disappointed with the results.

"I wasn't interested in the people they recommended," she said. "I think with our model I would get better recommendations."

The algorithm is similar to the one used by Netflix, which recommends movies based on a costumer's viewing history. Zhao said his algorithm also could be used for other services that match people with something they are searching for online, such as jobs and colleges.

Another phenomenon Zhao discovered was that his algorithm worked best for men with "athletic" body types connecting with women with "athletic" or "fit" body types, and for women who indicate they "want many kids." The model also works best for users who upload more photos of themselves.

"The more active you are" on the dating website, the better this algorithm is in selecting your partner, Zhao said. "The men who are more athletic are probably more active on the dating site because they are more confident, and the same is true for women who are more athletic or fit."

Zhao's study was coauthored by Mo Yu of Penn State University and Bo Gao of Beijing Jiaotong University. It will be published in an upcoming issue of IEEE Intelligent Systems.


Return to top of page