Learning faces to predict user attractiveness in an online dating market

Abstract

With the increasing use of online matching markets, predicting the matching probability among users is crucial for better market design. Although previous studies have constructed visual features to predict the matching probability, facial features extracted by deep learning have not been widely used. In an online dating market, from the user attractiveness prediction analysis, we find that deep learning-enabled facial features can significantly enhance a user’s ideal partner preferences prediction accuracy. We also predict the attractiveness at various evaluator groups and explain their different preferences based on the evolutionary psychology theory. Our work contributes to IS researchers utilizing facial features with deep learning and interpreting them to investigate underlying mechanisms in online matching markets. From a practical perspective, matching platforms can predict matching probability more accurately for better market design and recommender systems for maximizing the matching outcome.