REDUCING RECOMMENDATION INEQUALITY VIA TWO‐SIDED MATCHING: A FIELD EXPERIMENT OF ONLINE DATING
针对主流约会平台推荐算法导致的不平等问题，借鉴经济学匹配模型改进算法，通过大规模实地实验验证，新算法减少不平等、提高预测准确度并促成更多配对。
Abstract Leading dating platforms usually recommend only a small fraction of users based on users' popularity and similarity, leading to recommendation inequality. We use a stylized matching model from economics to modify existing algorithms to reduce inequality. We evaluate the proposed method through a large‐scale field experiment on a dating platform. Experiment results suggest that our recommender reduces inequality, improves predictive accuracy, and leads to substantially more matched couples than other competing algorithms.