A Self-Adaptive Fairness Constraint Framework for Industrial Recommender System

Achieving fairness among different individuals or groups is an essential task for industrial recommender systems. Due to the group's personalized selection tendencies and the non-uniform population distributions, existing industrial recommenders tend to make unfair predictions towards the preferences of minority groups. To alleviate this unfairness, we propose a model-agnostic self-adaptive fairness constraint framework (SaFair) based on the posterior preferences of different groups. We construct group-level and individual-level fairness constraints. The former measures consistency between group-level posterior preferences and predicted interests, and the latter relies on the degree of consistency in interests between a user and their associated group to perform self-adaptive constraints. In particular, to balance effectiveness and fairness, we utilize uncertainty estimation to adjust the intensity of constraints according to the model's learning status called self-adaptive constraints. Extensive offline experiments and online A/B Testing are conducted and the results validate the superiority of our proposed method over the baselines. SaFair has been successfully deployed in Kuaishou, one of China's most popular short-video streaming platforms with hundreds of millions of active users.