论文标题
超级*算法以优化同行评审中的纸质招标
A SUPER* Algorithm to Optimize Paper Bidding in Peer Review
论文作者
论文摘要
许多应用程序涉及用户的顺序到达，并需要向每个用户显示项目的订购。一个主要的示例（构成本文的重点）是会议同行评审中的投标过程，在该过程中，审阅者顺序输入系统，每个审阅者都需要显示提交的论文列表，然后审阅者“投标”来审查某些论文。所显示的论文的顺序对由于首要效应而对投标产生了重大影响。在决定订购论文以显示的情况下，有两个相互竞争的目标：（i）为每篇论文提供足够多的出价，以及（ii）通过向审阅者展示相关项目来满足审阅者。在本文中，我们首先要开发一个以原则性的方式研究此问题的框架。我们为此目标提出了一种由A*算法启发的算法，称为Super*。从理论上讲，我们显示了算法的局部最佳保证，并证明流行的基线非常优势。此外，在相似之处的社区模型下，我们证明超级*是最佳的*，而流行的基线是相当优势的。在ICLR 2018和合成数据的实际数据实验中，我们发现超级*的表现优于现有系统中部署的基准，始终将比必需出价少于50-75％或更多的论文数量减少，并且对各种现实世界中的复杂性也有强大的稳定性。
A number of applications involve sequential arrival of users, and require showing each user an ordering of items. A prime example (which forms the focus of this paper) is the bidding process in conference peer review where reviewers enter the system sequentially, each reviewer needs to be shown the list of submitted papers, and the reviewer then "bids" to review some papers. The order of the papers shown has a significant impact on the bids due to primacy effects. In deciding on the ordering of papers to show, there are two competing goals: (i) obtaining sufficiently many bids for each paper, and (ii) satisfying reviewers by showing them relevant items. In this paper, we begin by developing a framework to study this problem in a principled manner. We present an algorithm called SUPER*, inspired by the A* algorithm, for this goal. Theoretically, we show a local optimality guarantee of our algorithm and prove that popular baselines are considerably suboptimal. Moreover, under a community model for the similarities, we prove that SUPER* is near-optimal whereas the popular baselines are considerably suboptimal. In experiments on real data from ICLR 2018 and synthetic data, we find that SUPER* considerably outperforms baselines deployed in existing systems, consistently reducing the number of papers with fewer than requisite bids by 50-75% or more, and is also robust to various real world complexities.
