  Recommender systems, while transformative in online user experiences, have
raised concerns over potential provider-side fairness issues. These systems may
inadvertently favor popular items, thereby marginalizing less popular ones and
compromising provider fairness. While previous research has recognized
provider-side fairness issues, the investigation into how these biases affect
beyond-accuracy aspects of recommendation systems - such as diversity, novelty,
coverage, and serendipity - has been less emphasized. In this paper, we address
this gap by introducing a simple yet effective post-processing re-ranking model
that prioritizes provider fairness, while simultaneously maintaining user
relevance and recommendation quality. We then conduct an in-depth evaluation of
the model's impact on various aspects of recommendation quality across multiple
datasets. Specifically, we apply the post-processing algorithm to four distinct
recommendation models across four varied domain datasets, assessing the
improvement in each metric, encompassing both accuracy and beyond-accuracy
aspects. This comprehensive analysis allows us to gauge the effectiveness of
our approach in mitigating provider biases. Our findings underscore the
effectiveness of the adopted method in improving provider fairness and
recommendation quality. They also provide valuable insights into the trade-offs
involved in achieving fairness in recommender systems, contributing to a more
nuanced understanding of this complex issue.
