Browsing through the wedding section of the New York Times, the announcements follow a fairly standard formula – wedding date, info on work and family, and lastly, usually a line or two dedicated to how they met . While traditional meet cutes do make for more entertaining reads, it is more often than not that the couple met through an online dating service. But this is hardly the trend for just the glitzy young millennials whose beaming portraits are featured in the NYT. Currently, over 20% of heterosexual relationships and 70% of same-sex relationships in the US start on the internet, with this avenue quickly becoming the predominant method for meeting a significant other . Online dating companies hoping to capture a share of this growing market must therefore have a competitive edge.
The Online Dating Ecosystem
In this $4B industry, a few key players dominate the market. Match Group, the owner of OkCupid (as well as Match, Tinder, and 45 other dating businesses), accounts for roughly one third of the total market . Although OkCupid is part of the larger Match Group and its holding company IAC, it still needs to differentiate itself in order to remain relevant in a very crowded marketplace. OkCupid’s longer profiles and Q&As have allowed the company to identify itself as the go to place for users who are not simply looking for just a hookup, but who may also not be ready to get married right away. Newer entrants such as Hinge, however, have begun incorporating elements of OkCupid’s model. But what OkCupid has that isn’t easily replicable is its vast trove of customer data courtesy of the hundreds of questions its users have answered. It has historically used this data to determine compatibility between matches, but with machine learning, OkCupid has the potential to leverage this and other sources of user information to provide even more value.
Data at OkCupid
With a reputation for being a highly data-driven company, OkCupid has relied on its machine learning algorithms to connect people. A higher match percentage means that a couple will have a higher likelihood of clicking, with different weights given to different questions . But beyond simply producing matches, OkCupid also incorporates machine learning as a community improvement tool. Its support & moderation team monitors machine learning alerts that detect harmful or abusive language. With the aid of technology, OkCupid can respond quickly to instances of harassment while bringing in human moderators on an as-needed basis .
Currently, OkCupid has not defined a longer-term strategy to incorporate machine learning more deeply into its matching process. The team at OkCupid maintains that there will always be an element of chemistry that cannot be replicated online, and so their main goal is to connect people who already have a lot in common . However, with the advances in machine learning, an argument can be made for more sophisticated algorithms involving even more data points that can maybe tell us what we do not even know about ourselves.
From OkCupid to AmazingCupid
To determine what OkCupid could do in the context of machine learning, we must first understand the pitfalls that current dating apps face. Jeremy Arnold, co-founder of the now defunct dating startup Launch Social, illustrates the struggles that many singles have encountered in the following graphic :
Unsurprisingly, the reason most dating apps fail to eliminate these pain points is due to lying, whether intentionally or inadvertently. By relying solely on the answers to its questions, OkCupid assumes the user knows who they are and what they want. But personal biases and societal pressures can often lead to people answering questions in a deliberately unreliable way. One way to account for this is to link the data that OkCupid already has on a user to data from other social media sources to form a more holistic picture. OkCupid may never purposely call people out for discrepancies between their dating profile and their tweets, but if it can know what to weigh more, it can help to determine which is more likely to be true.
OkCupid’s distinguishing feature has been its focus not just on looks, but this is an area that machine learning can also help with. To do so in a way that still aligns with the company’s values, say that a user says she likes a certain height, but routinely messages people who are shorter. The algorithm can learn that this is not actually as much of a deal breaker for her as she originally thought, and start shifting its recommendations without notice.
But even as our online presence expands and more data becomes available, could machine learning algorithms ever progress to the point that we would trust its results? How far would we go to never go on a bad date again?
 “Binge Read Featured Couples.” Nytimes.com, 2018. [https://www.nytimes.com/spotlight/wedding-announcements], accessed Nov. 2018.
 “The Irresistible Rise of Online Dating.” Economist.com, Aug. 17, 2018. [https://www.economist.com/graphic-detail/2018/08/17/the-irresistible-rise-of-internet-dating], accessed Nov. 2018.
 Mangalindan, JP. “How Match got away with buying 25 dating sites — and counting.” Finance.yahoo.com, June 25, 2018. [https://finance.yahoo.com/news/match-group-can-get-away-acquiring-25-dating-sites-counting-151306438.html], accessed Nov. 2018.
 “Match Group.” IAC.com. [http://www.iac.com/brand/match-group], accessed Nov. 2018.
 “OkCupid Is Teaming Up With ACLU To Connect Like-minded Daters Looking For Love (and Justice).” PRNewswire.com, June 6, 2018. [https://www.prnewswire.com/news-releases/okcupid-is-teaming-up-with-aclu-to-connect-like-minded-daters-looking-for-love-and-justice-300660938.html], accessed Nov. 2018.
 Paul, Kari. “Why OkCupid wants to slow things down.” Marketwatch.com, Aug. 29, 2017. [https://www.marketwatch.com/story/why-okcupid-wants-to-slow-things-down-2017-08-29], accessed Nov. 2018.
 Arnold, Jeremy. “How could machine learning best be applied to online dating apps?” Quora.com, Dec. 29, 2017. [https://www.quora.com/How-could-machine-learning-best-be-applied-to-online-dating-apps], accessed Nov. 2018.