  Driven by the need to capture users' evolving interests and optimize their
long-term experiences, more and more recommender systems have started to model
recommendation as a Markov decision process and employ reinforcement learning
to address the problem. Shouldn't research on the fairness of recommender
systems follow the same trend from static evaluation and one-shot intervention
to dynamic monitoring and non-stop control? In this paper, we portray the
recent developments in recommender systems first and then discuss how fairness
could be baked into the reinforcement learning techniques for recommendation.
Moreover, we argue that in order to make further progress in recommendation
fairness, we may want to consider multi-agent (game-theoretic) optimization,
multi-objective (Pareto) optimization, and simulation-based optimization, in
the general framework of stochastic games.
