  Recommender system is a widely adopted technology in a diversified class of
product lines. Modern day recommender system approaches include matrix
factorization, learning to rank and deep learning paradigms, etc. Unlike many
other approaches, learning to rank builds recommendation results based on
maximization of the probability of ranking orders. There are intrinsic issues
related to recommender systems such as selection bias, exposure bias and
popularity bias. In this paper, we propose a fair recommender system algorithm
that uses Poisson process and Skellam distribution. We demonstrate in our
experiments that our algorithm is competitive in accuracy metrics and far more
superior than other modern algorithms in fairness metrics.
