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.

翻译：推荐系统虽深刻改变了在线用户体验，却也引发了提供者侧公平性问题。此类系统可能无意中偏袒热门项目，从而边缘化冷门项目，损害提供者公平性。尽管已有研究关注提供者侧公平问题，但其对推荐系统超准确度维度（如多样性、新颖性、覆盖率与惊喜度）的影响尚未充分探讨。本文通过提出一种简单有效的后处理重排序模型来填补这一空白，该模型在优先保障提供者公平性的同时，兼顾用户相关性与推荐质量。我们随后跨多数据集深入评估该模型对推荐质量各维度的影响：具体而言，将后处理算法应用于四类不同领域数据集的四种推荐模型，系统评估准确度与超准确度两方面指标的提升效果。这种综合分析使我们得以衡量该方法缓解提供者偏见的有效性。研究结果证实了所采用方法在提升提供者公平性与推荐质量方面的效力，同时为理解推荐系统公平性实现过程中的权衡关系提供了宝贵洞见，有助于深化对这一复杂问题的认知。