Is Romantic Desire Predictable? Machine Learning Applied to Initial Romantic Attraction

Samantha Joel*, Paul W. Eastwick, Eli J. Finkel

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


Matchmaking companies and theoretical perspectives on close relationships suggest that initial attraction is, to some extent, a product of two people’s self-reported traits and preferences. We used machine learning to test how well such measures predict people’s overall tendencies to romantically desire other people (actor variance) and to be desired by other people (partner variance), as well as people’s desire for specific partners above and beyond actor and partner variance (relationship variance). In two speed-dating studies, romantically unattached individuals completed more than 100 self-report measures about traits and preferences that past researchers have identified as being relevant to mate selection. Each participant met each opposite-sex participant attending a speed-dating event for a 4-min speed date. Random forests models predicted 4% to 18% of actor variance and 7% to 27% of partner variance; crucially, however, they were unable to predict relationship variance using any combination of traits and preferences reported before the dates. These results suggest that compatibility elements of human mating are challenging to predict before two people meet.

Original languageEnglish
Pages (from-to)1478-1489
Number of pages12
JournalPsychological science
Issue number10
Publication statusPublished - 1 Oct 2017
Externally publishedYes


  • attraction
  • dating
  • ensemble methods
  • machine learning
  • open data
  • open materials
  • random forests
  • romantic desire
  • romantic relationships
  • speed dating
  • statistical learning


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