  Data bias, e.g., popularity impairs the dynamics of two-sided markets within
recommender systems. This overshadows the less visible but potentially
intriguing long-tail items that could capture user interest. Despite the
abundance of research surrounding this issue, it still poses challenges and
remains a hot topic in academic circles. Along this line, in this paper, we
developed a re-ranking approach in dynamic settings with fair-exposure
optimization driven by strategic agents. Designed for the producer side, the
execution of agents assumes content creators can modify item features based on
strategic incentives to maximize their exposure. This iterative process entails
an end-to-end optimization, employing differentiable ranking operators that
simultaneously target accuracy and fairness. Joint objectives ensure the
performance of recommendations while enhancing the visibility of tail items. We
also leveraged the performativity nature of predictions to illustrate how
strategic learning influences content creators to shift towards fairness
efficiently, thereby incentivizing features of tail items. Through
comprehensive experiments on both public and industrial datasets, we have
substantiated the effectiveness and dominance of the proposed method especially
on unveiling the potential of tail items.
