# Optimal Cupid

## 2014

Chris McKinlay used Python scripts to riffle through hundreds of OkCupid survey questions. He then sorted female daters into seven clusters, like “Diverse” and “Mindful,” each with distinct characteristics. — image: Maurico Alejo

(Poulsen, 2014)

OkCupid Homepage

(OkCupid, 2014)

I used the data I’d scraped and a machine learning algorithm called AdaBoost to optimize which questions I chose and the associated weights that I gave them.

(McKinlay, 2004)
The process I've just described is a version of an optimization algorithm called simulated annealing. simulated annealing is a robust method for finding approximately optimal solutions in large search spaces, such as the space of all possible responses to match questions on OKC. [...] This process will effectively orient your profile in the (very high dimensional) OkC question space in a way that is consistent with your actual goals.

(McKinlay, 2004)
I love OkCupid. Their stated purpose is: 'We use math to get you dates.' It should be: 'You use math to get your own dates.' I used math to improve my OkC experience and went on 88 dates from the site in three months. I went from an OkCupid 'match percentage' at or above 90% with a few hundred women in L.A. to matching over 30,000 women at that level. It was like stepping into a giant spotlight of female attention.

(McKinlay, 2004)

McKinlay, C. (2014) *Optimal Cupid*. CreateSpace.

OkCupid (2014) *okcupid.com*, 1 February. [link]

Poulsen, K. (2014) *How a Math Genius Hacked OkCupid to Find True Love*, Wired, 21 January. [link]