Beyond homophily: AI-driven analysis of facial and personality similarity preferences in online dating

Abstract

Online dating platforms increasingly mediate human connection, yet the mechanisms by which users evaluate subjective traits like appearance and personality remain underexplored. To investigate these preference dynamics, this study applies AI-driven methods to a dataset of 506,014 interactions from 41,441 users on a major heterosexual dating platform in South Korea. By employing computer vision and large language models to quantify facial and personality similarity, our mixed-effects analysis reveals a significant gender asymmetry: women prefer facial similarity (homophily), whereas men exhibit a preference for facial dissimilarity (heterophily). Furthermore, personality preferences are found to be context-dependent; a partner's socioeconomic status moderates the demand for similarity, amplifying the preference for women while attenuating it for men in upward evaluation contexts. These findings offer actionable implications for the design of matching systems, suggesting that preferences in digitally mediated environments function as adaptive strategies shaped by gender and contextual cues.