  Recommendation algorithms typically build models based on historical
user-item interactions (e.g., clicks, likes, or ratings) to provide a
personalized ranked list of items. These interactions are often distributed
unevenly over different groups of items due to varying user preferences.
However, we show that recommendation algorithms can inherit or even amplify
this imbalanced distribution, leading to unfair recommendations to item groups.
Concretely, we formalize the concepts of ranking-based statistical parity and
equal opportunity as two measures of fairness in personalized ranking
recommendation for item groups. Then, we empirically show that one of the most
widely adopted algorithms -- Bayesian Personalized Ranking -- produces unfair
recommendations, which motivates our effort to propose the novel fairness-aware
personalized ranking model. The debiased model is able to improve the two
proposed fairness metrics while preserving recommendation performance.
Experiments on three public datasets show strong fairness improvement of the
proposed model versus state-of-the-art alternatives.
  This is paper is an extended and reorganized version of our SIGIR
2020~\cite{zhu2020measuring} paper. In this paper, we re-frame the studied
problem as `item recommendation fairness' in personalized ranking
recommendation systems, and provide more details about the training process of
the proposed model and details of experiment setup.
