New algorithm to recommend best dating partner

Scientists have developed a new algorithm for dating sites that uses a person's contact history and on-line mating success to recommend partners with whom they may be more amorously compatible.
Researchers Kang Zhao and Xi Wang from the University of Iowa developed the algorithm that uses a person's past interests to recommend more compatible partners.
The algorithm is 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 on-line dating company.
It looked at 475,000 initial contacts involving 47,000 users in two US cities over a 196-day span. Of the users, 28,000 were men and 19,000 were women, and men made 80 per cent of the initial contacts.

Zhao said the data suggests that only about 25 per cent 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 said, 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.
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.
"Your actions reflect your taste and attractiveness in a way that could be more accurate than what you include in your profile," Zhao said.
Zhao's algorithm notices that while a client says he likes tall women, he keeps contacting short women, and will change its recommendations to him accordingly.

"In our model, users with similar taste and (un)attractiveness will have higher similarity scores than those who only share common taste or attractiveness," Zhao said.
"The model also considers the match of both taste and attractiveness when recommending dating partners. Those who match both a service user's taste and attractiveness are more likely to be recommended than those who may only ignite unilateral interests," said Zhao.