Tag Archives: facial feature analysis

VISAGE: Designing AI Artifacts for Dynamic Self-Presentation in Matching Platforms

Kwon, Soonjae, Gene Moo Lee, Dongwon Lee, Sung-Hyuk Park (2024) “VISAGE: Designing AI Artifacts for Dynamic Self-Presentation in Matching Platforms,” Working Paper.

  • Previous title: Learning Faces to Predict Matching Probability in an Online Dating Market
  • Presentations: DS (2021), AIMLBA (2021), WITS (2021), ICIS (2022)
  • Preliminary version in ICIS 2022 Proceedings
  • Based on an industry collaboration

Online matching platforms constrain users to static profiles, producing a mismatch between the idealized self a user presents and the heterogeneous preferences of potential partners. Drawing on self-discrepancy theory, we conceptualize this mismatch as an interpersonal gap between one’s presented self and what each partner desires to see, with AI serving as a mediator to help address it. Following the computational design science perspective, we propose VISAGE, an AI system comprising two artifacts grounded in distinct human-AI collaboration principles. The augmentation artifact selects optimal images from users’ existing assets, whereas the assemblage artifact generates new images tailored to individual partner preferences. Using large-scale operational data from a major online dating platform, we evaluate VISAGE at both the user and platform levels. At the user level, model-predicted ratings suggest that both artifacts improve attractiveness ratings. Relative effectiveness varies with partner-preference heterogeneity and user impression management skill, consistent with theoretical predictions from the human-AI collaboration literature. At the platform level, agent-based simulations suggest that VISAGE can enhance matching efficiency and reduce inequality in matching opportunities, although optimal deployment strategies depend on the platform’s recommendation algorithm. The theoretical contribution of this study is to foreground the interpersonal gap as a key source of matching inefficiency and to illustrate how AI can address it at scale. The design contribution lies in actionable design knowledge, including when to deploy augmentation versus assemblage artifacts based on user and partner characteristics and how to align user-facing AI features with backend algorithmic infrastructure.