  As Recommender Systems (RS) influence more and more people in their daily
life, the issue of fairness in recommendation is becoming more and more
important. Most of the prior approaches to fairness-aware recommendation have
been situated in a static or one-shot setting, where the protected groups of
items are fixed, and the model provides a one-time fairness solution based on
fairness-constrained optimization. This fails to consider the dynamic nature of
the recommender systems, where attributes such as item popularity may change
over time due to the recommendation policy and user engagement. For example,
products that were once popular may become no longer popular, and vice versa.
As a result, the system that aims to maintain long-term fairness on the item
exposure in different popularity groups must accommodate this change in a
timely fashion.
  Novel to this work, we explore the problem of long-term fairness in
recommendation and accomplish the problem through dynamic fairness learning. We
focus on the fairness of exposure of items in different groups, while the
division of the groups is based on item popularity, which dynamically changes
over time in the recommendation process. We tackle this problem by proposing a
fairness-constrained reinforcement learning algorithm for recommendation, which
models the recommendation problem as a Constrained Markov Decision Process
(CMDP), so that the model can dynamically adjust its recommendation policy to
make sure the fairness requirement is always satisfied when the environment
changes. Experiments on several real-world datasets verify our framework's
superiority in terms of recommendation performance, short-term fairness, and
long-term fairness.
