Kwon, Soonjae, Gene Moo Lee, Dongwon Lee, Sung-Hyuk Park (2024) “Digital Cupid: Empowering Generative AI for Fair and Efficient Matchmaking,” 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

With the increasing prevalence of online transactions, enhancing matching efficiency has emerged as a critical objective for most matching platforms. However, these efforts often lead to decreased fairness, making it challenging to balance these two elements. This study presents a novel generative AI-based approach to increase the platform’s efficiency and fairness simultaneously in the context of online dating. By developing a model that utilizes users’ multimodal features to predict individual preferences, we assess the impact of various matching algorithms on platform efficiency and fairness. Extensive simulations show that our fairness-aware algorithm significantly enhances both metrics, addressing conventional methods’ severe efficiency-fairness tradeoff issue. We also introduce a novel generative AI-based personalization technique that modifies users’ profile images in different directions according to their counterparts, further boosting efficiency without sacrificing fairness. Our matching framework can be applied to platforms with various objectives, contributing to all stakeholders in digital platforms.