A Fair Enhanced Bayesian Personalized Ranking Using Adversarial Learning

The ranking task is the critical step performed during a recommendation process to predict the top-list of most-wanted products for users. Learn-to-rank algorithms have been developed to refine the ranking process. However, the underrepresentation of some demographic user categories leads to unwanted biased ranking performances that affect the fairness aspects of the recommendation. Bayesian Pairwise Ranking (BPR) is among the most popular ranking algorithms for its important ranking accuracy performance. BPR with machine learning recommendation models can unfairly perform for minority user groups. We tackle the unfairness in the recommendation by proposing the FEBPR method. Our proposal is a fair pairwise Bayesian ranking in which the data debiasing is performed by using adversarial learning fed by enriched embeddings. In our proposal, user and item embeddings are learned to obey the adversarial constraint and mislead the adversary classifier that should not be able to have a priori assumptions about user membership. Extensive experiments are performed on real-world datasets and show that the performances of the proposed debiasing method improve fairness ranking aspects, and therefore the recommendation fairness. It is also shown that our proposal outperforms state-of-the-art fairness ranking methods and presents an interesting trade-off between the fairness aspects and ranking accuracy.