Assigning qualified, unbiased and interested reviewers to paper submissions
is vital for maintaining the integrity and quality of the academic publishing
system and providing valuable reviews to authors. However, matching thousands
of submissions with thousands of potential reviewers within a limited time is a
daunting challenge for a conference program committee. Prior efforts based on
topic modeling have suffered from losing the specific context that help define
the topics in a publication or submission abstract. Moreover, in some cases,
topics identified are difficult to interpret. We propose an approach that
learns from each abstract published by a potential reviewer the topics studied
and the explicit context in which the reviewer studied the topics. Furthermore,
we contribute a new dataset for evaluating reviewer matching systems. Our
experiments show a significant, consistent improvement in precision when
compared with the existing methods. We also use examples to demonstrate why our
recommendations are more explainable. The new approach has been deployed
successfully at top-tier conferences in the last two years.

本文提出了一种基于上下文学习的评审人匹配方法，解决了用主题建模进行评审者指派时主题上下文信息丢失和难以解释的问题，并在实验中取得了显著的精度提高。

面向审稿人推荐系统的提交感知评审人画像

Submission-Aware Reviewer Profiling for Reviewer Recommender System

Many conferences rely on paper bidding as a key component of their reviewer
assignment procedure. These bids are then taken into account when assigning
reviewers to help ensure that each reviewer is assigned to suitable papers.
However, despite the benefits of using bids, reliance on paper bidding can
allow malicious reviewers to manipulate the paper assignment for unethical
purposes (e.g., getting assigned to a friend's paper). Several different
approaches to preventing this manipulation have been proposed and deployed. In
this paper, we enumerate certain desirable properties that algorithms for
addressing bid manipulation should satisfy. We then offer a high-level analysis
of various approaches along with directions for future investigation.

本文讨论了学术期刊中计算机科学方面论文竞标的问题，提出了现有算法中对竞标操纵的防范策略，并列举了算法需要满足的特点并提出了未来的研究方向。

论文评审中防止操纵的权衡

Tradeoffs in Preventing Manipulation in Paper Bidding for Reviewer Assignment

We consider three important challenges in conference peer review: (i)
reviewers maliciously attempting to get assigned to certain papers to provide
positive reviews, possibly as part of quid-pro-quo arrangements with the
authors; (ii) "torpedo reviewing," where reviewers deliberately attempt to get
assigned to certain papers that they dislike in order to reject them; (iii)
reviewer de-anonymization on release of the similarities and the
reviewer-assignment code. On the conceptual front, we identify connections
between these three problems and present a framework that brings all these
challenges under a common umbrella. We then present a (randomized) algorithm
for reviewer assignment that can optimally solve the reviewer-assignment
problem under any given constraints on the probability of assignment for any
reviewer-paper pair. We further consider the problem of restricting the joint
probability that certain suspect pairs of reviewers are assigned to certain
papers, and show that this problem is NP-hard for arbitrary constraints on
these joint probabilities but efficiently solvable for a practical special
case. Finally, we experimentally evaluate our algorithms on datasets from past
conferences, where we observe that they can limit the chance that any malicious
reviewer gets assigned to their desired paper to 50% while producing
assignments with over 90% of the total optimal similarity. Our algorithms still
achieve this similarity while also preventing reviewers with close associations
from being assigned to the same paper.

研究了会议同行评审中的三个挑战：评审人恶意加入给出正面评价的论文、评审人故意给不喜欢的论文打低分和评审人匿名解除。提出了一种对三个问题进行通用性整合的方法，并针对给定限制下的评审人分配问题提出了随机算法，成功限制了恶意评审人分配。

通过随机评审分配来缓解同行评审中的操纵问题

Mitigating Manipulation in Peer Review via Randomized Reviewer  Assignments

Paper-reviewer recommendation task is of significant academic importance for
conference chairs and journal editors. How to effectively and accurately
recommend reviewers for the submitted papers is a meaningful and still tough
task. In this paper, we propose a Multi-Label Classification method using a
hierarchical and transparent Representation named Hiepar-MLC. Further, we
propose a simple multi-label-based reviewer assignment MLBRA strategy to select
the appropriate reviewers. It is interesting that we also explore the
paper-reviewer recommendation in the coarse-grained granularity.

本文提出了一种多标签分类的方法，使用分层透明表示方法，并提出了一种简单的基于多标签的评审人分配策略，以选择适当的评审人，同时还在粗粒度的粒度中探索了论文 - 评审人推荐。