On one hand, online dating websites connect users simultaneously to hundreds or thousands of profiles, promising enormous expansion in partner diversity. On the other, filtering and algorithmic matchmaking introduces risk for the pool of partners to be less diverse by ethnicity, by personality, or by any other (potentially irrelevant) input to the black-box models. So which is it? More diversity or more similarity?
Matchmaking has existed for millennia, but in the 21st century, the search for love has gone online, and for some individuals is now mediated by sophisticated mathematical algorithms. Under traditional forms of matchmaking, third parties – religious leaders, parents and other connections within a closed-form social network – recommended romantic partners from a narrow pool of individuals (Finkel et al., 2012). Selection from this restricted `field of eligibles’ (Kerckhoff, 1964), endorsed endogamy where partners from the same social group (ethnicity, religion, or culture) come together, making exogamy, the act of marrying a diverse partner, a rarity. The field-of-eligibles hypothesis is upheld as an explaination for spousal correlations, in contrast to the possibility of similarity preference in partner attributes (Berscheid & Reis, 1998). Assortive matching, where potential matches share educational or economic circles (Becker, 1973), occurs even in populations with no individual preference for homogeneity (Burley, 1983).
The advent of online dating has changed the fundamentals of searching for prospective partners, altering both the romantic acquaintance process and the compatibility matching process. The platform is not only penetrating more of society with over 41% of the world active (Paisley 2018) but also more socially accepted (Whitty and Carr 2006). The legacy of such sites is beginning to emerge with 20% of current committed romantic relationships beginning online (Xie et al., 2014). In particular, the pervasiveness of online dating has expanded users’ access to potential romantic partners which they would otherwise be unlikely to encounter. The Internet permits not only a death of distance geographically, with the ability to communicate without face-to-face encounters, but also death of distance in social network interactions. The pool is no longer defined by community or culture. Applying Feld’s focus theory, online dating is a hyper-focus platform, compared to traditional foci characterised by socially homogenous groups- such as religious congregations, workplaces or nightclubs (Feld 1982; Schmitz 2014). Online interactions can go beyond an existing network, introducing the potential for social discovery among high socio-structural heterogeneity. A priori, the case for greater diversity in online dating is clear.
With great choice, comes great choice fatigue, self-selection and intuition are unfeasible with such a large array of potential partners. In China, out of its 200 million single people, a quarter (54 million) used online dating services in 2016. The ability to browse and potentially match with over 50 million people is a daunting prospect. Instead, dating websites offer new choice infrastructures in filtering and recommending but both restrict diversity of viewed profiles.
Filter theory, proposed by Kerchoff and Davis (1962), describes homogeny in partner selection where people interact partners filtered by similarity of social demographic factors. Online search introduces greater scope for individuals to filter over selective and defined criteria, enforcing an unprecedented degree of parametrisation of potential partners. Precise attribute selection tools allow dating site users to eliminate huge groups of the population who don’t meet specific desires. However, by creating extensive checklists they may be closing their minds to possibilities, especially given the compression of compatibility criteria to modular attributes such as education or income, excluding important offline forms of self-presentation such as facial expressions or humour (Schmitz 2014). Some studies exist on how this filtering mechanism operationally affects successful matches, with Rudder (2014) considering how probability of messages or likes are derived from different partner attributes.
Technology has expanded the inputs to matchmaking, where not only hundreds of profile traits but also second-by-second user interaction behaviours can be used in mathematical algorithms to recommend potential partners. These digital traces introduce new complexity into designs of recommer systems. Content and collaborative filtering algorithms have wide commercial applications, working under the assumption `if you like person x, you will like person y’. However, unlike purchase or movie recommendations, successful matches critically require a ‘double coincidence of wants’ (Hitsch et al., 2010). Reciprocal recommenders such as RECON (Pizzato et al., 2011) offer higher success rates of conversion between recommendations, initiations and matches. Distance scoring systems attempt to minimise the Euclidean distance between partners across attributes (Hu et al., 2019). What these systems have in common is a structural design based on likeness. As Finkel et al., (2012) summarises, “[s]imilarity is a potent principle in online dating.” The recommended set of partners may indeed be even more homogenous than the traditional field of eligibles. Consider a dating website which demands users fill out a personality test, measuring attributes such as morality, extraversion or self-confidence. A matching algorithm could purposefully not recommend partners who misalign on these measures despite their potential compatibility in the real world. In fact, self-expansion theory argues people gain confidence from acceptance by dissimilar partners (Aron & Aron, 1997; Aron et al., 2006). As such, although users believe they can access a wider field, the irony is they are actually accessing users much more similar to themselves. The potential for recommender systems to diminish diversity has considerable consequence. Across social science literature, evidence suggests contact with dissimilar others and expansion of information flows beyond a close social network can broaden perspective and deepen empathy for other racial, religions or socioeconomic groups (Wright et al., 1997). Resnick et al., (2013) consider recommender systems as creators of content bubbles, responsible for the entrenchment of inaccurate or polarised beliefs. If recommender systems are trained on implicit feedback data such as clicks or messages, the training data is pre-biased by the filters already applied by the user, reducing diversity and impeding social discovery yet further.