Online Post-Processing In Rankings For Fair Utility Maximization

We consider the problem of utility maximization in online ranking applications while also satisfying a given fairness constraint. We are given batches of items arriving over time, already ranked using an existing ranking model. We propose online post-processing for re-ranking these batches to enforce adherence to a given fairness constraint, while maximizing our notion of utility. To achieve this goal, we propose two deterministic re-ranking policies. In addition, we learn a re-ranking policy based on a novel variation of learning to search. Extensive experiments on real world and synthetic datasets demonstrate the effectiveness of our proposed policies both in terms of adherence to the fairness constraint and utility maximization. Furthermore, our analysis shows that the performance of the proposed policies depends on the original data distribution w.r.t the given fairness constraint and the notion of utility.