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Recommender system is a widely adopted technology in a diversified class ofproduct lines. Modern day recommender system approaches include matrixfactorization, learning to rank and deep learning paradigms, etc. Unlike manyother approaches, learning to rank builds recommendation results based onmaximization of the probability of ranking orders. There are intrinsic issuesrelated to recommender systems such as selection bias, exposure bias andpopularity bias. In this paper, we propose a fair recommender system algorithmthat uses Poisson process and Skellam distribution. We demonstrate in ourexperiments that our algorithm is competitive in accuracy metrics and far moresuperior than other modern algorithms in fairness metrics.

Skellam Rank: Fair Learning to Rank Algorithm Based on Poisson Process and Skellam Distribution for Recommender Systems