AI 中文总结
针对公平排名聚合中现有研究多假设排名无平局的问题，提出后处理方法，通过精确算法和快速启发式算法，将不公平的带平局共识排名转化为最接近的公平共识排名。
AI 中文摘要
寻找公平共识排名是公平排名聚合领域的活跃研究课题，已有较多研究，但现有研究大多假设输入和输出排名均为元素严格有序的排列。实际中带平局的排名更为常见，本研究针对此缺口，提出一种后处理方法，当给定不公平的考虑平局的共识排名时，确定最接近的公平共识排名，还提出了精确算法和快速启发式算法来实现该目标。
英文摘要
The problem of finding a fair consensus ranking is an active research topic in the domain of fair rank aggregation and has been well studied; however, existing studies predominantly consider both the input rankings and the output ranking to be permutations where elements are always strictly ordered. In practice, however, a ranking with ties is far more common. This study bridges this gap by presenting a post-processing approach to determine the closest fair consensus ranking when an unfair tie-aware consensus ranking is provided. It proposes an exact algorithm and a fast heuristic to achieve this.
