While technological solutions have led to increased efficiency, online dating services have not been able to decrease the time needed to find a suitable match. Online dating users spend on average 12 hours a week online on dating activity . Hinge, for example, found that only 1 in 500 swipes on its platform led to an exchange of phone numbers . If Amazon can recommend products and Netflix can provide movie suggestions, why can’t online dating services harness the power of data to help users find optimal matches? Like Amazon and Netflix, online dating services have a plethora of data at their disposal that can be employed to identify suitable matches. Machine learning has the potential to improve the product offering of online dating services by reducing the time users spend identifying matches and increasing the quality of matches.
Hinge: A Data Driven Matchmaker
Hinge has released its “Most Compatible” feature which acts as a personal matchmaker, sending users one recommended match per day. The company uses data and machine learning algorithms to identify these “most compatible” matches .
How does Hinge know who is a good match for you? It uses collaborative filtering algorithms, which provide recommendations based on shared preferences between users . Collaborative filtering assumes that if you liked person A, then you will like person B because other users that liked A also liked B . Thus, Hinge leverages your individual data and that of other users to predict individual preferences. Studies on the use of collaborative filtering in online dating show that it increases the probability of a match . In the same way, early market tests have shown that the Most Compatible feature makes it 8 times more likely for users to exchange phone numbers .
Hinge’s product design is uniquely positioned to make use of machine learning capabilities. Machine learning requires large volumes of data. Unlike popular services such as Tinder and Bumble, Hinge users don’t “swipe right” to indicate interest. Instead, they like specific parts of a profile including another user’s pictures, videos, or fun facts. By allowing users to provide specific “likes” as opposed to single swipe, Hinge is accumulating larger volumes of data than its competitors.
When a user enrolls on Hinge, he or she must create a profile, which is based on self-reported pictures and information. However, caution should be taken when using self-reported data and machine learning to find dating matches.
Explicit versus Implicit Preferences
Prior machine learning studies show that self-reported traits and preferences are poor predictors of initial romantic desire . One possible explanation is that there may exist traits and preferences that predict desirability, but that we are unable to identify them . Research also shows that machine learning provides better matches when it uses data from implicit preferences, as opposed to self-reported preferences .
Hinge’s platform identifies implicit preferences through “likes”. However, it also allows users to disclose explicit preferences such as age, height, education, and family plans. Hinge may want to continue using self-disclosed preferences to identify matches for new users, for which it has little data. However, it should seek to rely primarily on implicit preferences.
Self-reported data may also be inaccurate. This may be particularly relevant to dating, as individuals have an incentive to misrepresent themselves to attain better matches , . In the future, Hinge may want to use outside data to corroborate self-reported information. For example, if a user describes him or herself as athletic, Hinge could request the individual’s Fitbit data.
The following questions require further inquiry:
- The effectiveness of Hinge’s match making algorithm relies on the existence of identifiable factors that predict romantic desires. However, these factors may be nonexistent. Our preferences may be shaped by our interactions with others . In this context, should Hinge’s objective be to find the perfect match or to increase the number of personal interactions so that individuals can subsequently define their preferences?
- Machine learning capabilities can allow us to uncover preferences we were unaware of. However, it can also lead us to uncover undesirable biases in our preferences. By providing us with a match, recommendation algorithms are perpetuating our biases. How can machine learning allow us to identify and eliminate biases in our dating preferences?
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