FairBayRank: A Fair Personalized Bayesian Ranker
Armielle Noulapeu Ngaffo, Julien Albert, Benoît Frénay, Gilles Perrouin · 2023
Recommender systems are data-driven models that successfully provide users with personalized rankings of items (movies, books...).Meanwhile, for user minority groups, those systems can be unfair in predicting users' expectations due to biased data.Consequently, fairness remains an open challenge in the ranking prediction task.To address this issue, we propose in this paper FairBayRank, a fair Bayesian personalized ranking algorithm that deals with both fairness and ranking performance requirements.FairBayRank evaluation on real-world datasets shows that it efficiently alleviates unfairness issues while ensuring high prediction performances.