Automatically matching reviewers to papers is a crucial step of the peer
review process for venues receiving thousands of submissions. Unfortunately,
common paper matching algorithms often construct matchings suffering from two
critical problems: (1) the group of reviewers assigned to a paper do not
collectively possess sufficient expertise, and (2) reviewer workloads are
highly skewed. In this paper, we propose a novel local fairness formulation of
paper matching that directly addresses both of these issues. Since optimizing
our formulation is not always tractable, we introduce two new algorithms,
FairIR and FairFlow, for computing fair matchings that approximately optimize
the new formulation. FairIR solves a relaxation of the local fairness
formulation and then employs a rounding technique to construct a valid matching
that provably maximizes the objective and only compromises on fairness with
respect to reviewer loads and papers by a small constant. In contrast, FairFlow
is not provably guaranteed to produce fair matchings, however it can be 2x as
efficient as FairIR and an order of magnitude faster than matching algorithms
that directly optimize for fairness. Empirically, we demonstrate that both
FairIR and FairFlow improve fairness over standard matching algorithms on real
conference data. Moreover, in comparison to state-of-the-art matching
algorithms that optimize for fairness only, FairIR achieves higher objective
scores, FairFlow achieves competitive fairness, and both are capable of more
evenly allocating reviewers.

本文提出了基于局部公平性的论文匹配新方法，并引入了两个新的算法 FairIR 和 FairFlow，相对于已有算法，实验证明 FairIR 和 FairFlow 的效果更优，能够更公平地分配评审者。