  Learning to rank is an effective recommendation approach since its
introduction around 2010. Famous algorithms such as Bayesian Personalized
Ranking and Collaborative Less is More Filtering have left deep impact in both
academia and industry. However, most learning to rank approaches focus on
improving technical accuracy metrics such as AUC, MRR and NDCG. Other
evaluation metrics of recommender systems like fairness have been largely
overlooked until in recent years. In this paper, we propose a new learning to
rank algorithm named Pareto Pairwise Ranking. We are inspired by the idea of
Bayesian Personalized Ranking and power law distribution. We show that our
algorithm is competitive with other algorithms when evaluated on technical
accuracy metrics. What is more important, in our experiment section we
demonstrate that Pareto Pairwise Ranking is the most fair algorithm in
comparison with 9 other contemporary algorithms.
