Not long after, in 2004, OkCupid began https://www.datingranking.net/es/citas-lesbianas/ offering algorithmic matching alongside the basic search functionality that users had come to expect from earlier sites. The combination of searching and matching on OkCupid meant the algorithm functioned as more of a decision aid by empowering users to seek out potential partners for themselves while also offering suggestions to narrow the field (Tong et al., 2016). OkCupid’s algorithm used “match percentages” to assess compatibility based on how users answered questions and how they wanted potential partners to answer, and then it weighted each question by its reported level of importance (i.e., “irrelevant,” “a little,” “somewhat,” “very”; Rudder, 2013). The data came from an assortment of questions (e.g., “Are you afraid of death?” “Would you date someone who keeps a gun in the house?”), including those unlikely to be found on a scientific questionnaire (e.g., “Do you believe in dinosaurs?” “Is astrological sign at all important in a match?”; Cooper, 2017). By assuming the answers to some questions were more important than others, OkCupid gave users control over the matching process and the ability to provide input into how their data were used by the site’s algorithm.
The problem with these early matching systems is that they assumed users knew precisely what they desired in a partner. However, people’s stated preferences for an ideal mate do not always align with what they find attractive in person (Eastwick Finkel, 2008). g., height, income) that are poor indicators of what it will be like to interact with someone in the flesh (Frost et al., 2008). , 2021).
“It’s scary to know how much it’ll affect people. I try to ignore some of it, or I’ll go insane. We’re getting to the point where we have a social responsibility to the world because we have this power to influence it.” –Jonathan Badeen, Tinder 2
Many online dating sites have since started using more sophisticated machine learning algorithms to predict users’ preferences from implicit forms of feedback (Dinh et al
The release of the iPhone in 2007 and subsequent launch of Grindr in 2009 marked a seismic shift in the industry from online dating sites to mobile dating apps. Unlike their predecessors, dating apps required a quick sign-up process, prompting developers to turn to collaborative filtering to gain insight into their users’ preferences. Collaborative filtering algorithms work by delivering recommendations based on the behaviors of users who appear to have similar tastes (Krzywicki et al., 2015). For example, imagine a hypothetical scenario where Tyrone is attracted to Carlos. If others who like Carlos also show an interest in Zach, then Zach will be presented to Tyrone as a possible match. This strategy is used to suggest products on Amazon and movies on Netflix, but on dating apps, recommendations must be reciprocal to minimize rejection (Pizzato et al., 2013). In other words, matching algorithms must consider not only whether one person is likely to find another attractive but also whether that interest will be well received. Collaborative filtering is commonly used for matching on popular dating apps such as Tinder and Hinge (Lau Akkaraju, 2019).
Tinder claims to have retired Elo scores but provides few details about its new system (Tinder, 2019)
Launched in 2012, Tinder is known for its gamified approach to dating and its emphasis on hookups and casual relationships, although it is no longer just a ‘hookup app.’ The Tinder app is designed to mirror a deck of playing cards where users can swipe left to “keep playing” and right to match, with a double opt-in system used to confirm both partners are interested before they can begin messaging (Myles, 2020). Like other games of skill, Tinder uses the Elo system (Elo, 1978) to rate the desirability of users and match them with others who are in roughly the same league (Carr, 2016). The Elo system comes from chess, where it is used to assign players a score based on their prior wins/losses and the skill levels of their opponents (Glickman, 1995). On Tinder, ratings work similarly, with a right swipe from someone desirable having the greatest impact on a user’s score, just as a win against a Grandmaster in chess would matter more than beating an amateur player (Bartlett, 2020).
