Now, algorithm to bag best dating partner

Washington – Researchers have developed an algorithm for dating sites that uses a person’s contact history to recommend partners with whom they may be more amorously compatible.

Kang Zhao, assistant professor of management sciences in the Tippie College of Business, and University of Iowa doctoral student Xi Wang’s algorithm’s similar to the model Netflix uses to recommend movies users might like by tracking their viewing history.

Zhao’s team used data provided by a popular commercial online dating company whose identity is being kept confidential.

It looked at 475,000 initial contacts involving 47,000 users in two U.S. cities over a 196-day span. Of the users, 28,000 were men and 19,000 were women, and men made 80 percent of the initial contacts.

Zhao says the data suggests that only about 25 percent of those initial contacts were actually reciprocated.

To improve that rate, Zhao’s team developed a model that combines two factors to recommend contacts: a client’s tastes, determined by the types of people the client has contacted; and attractiveness/unattractiveness, determined by how many of those contacts are returned and how many are not.

Those combinations of taste and attractiveness, Zhao says, do a better job of predicting successful connections than relying on information that clients enter into their profile, because what people put in their profile may not always be what they’re really interested in.

They could be intentionally misleading, or may not know themselves well enough to know their own tastes in the opposite sex, Zhao theorizes.

So a man who says on his profile that he likes tall women may in fact be approaching mostly short women, even though the dating website will continue to recommend tall women.