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.

中文翻译：

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一种在同行评审中优化纸质投标的 SUPER* 算法

许多应用程序涉及用户的顺序到达，并且需要向每个用户显示项目的排序。一个主要的例子（构成本文的重点）是会议同行评审中的竞标过程，审稿人依次进入系统，需要向每个审稿人显示提交的论文列表，然后审稿人“竞标”以审阅一些论文. 由于首要效应，所显示论文的顺序对投标有重大影响。在决定论文的展示顺序时，有两个相互竞争的目标：（i）为每篇论文获得足够多的投标，以及（ii）通过向审稿人展示相关项目来满足他们。在本文中，我们首先开发一个框架，以有原则的方式研究这个问题。我们提出了一种名为 SUPER* 的算法，其灵感来自 A* 算法，用于实现此目标。从理论上讲，我们展示了我们算法的局部最优保证，并证明流行的基线相当次优。此外，在相似性的社区模型下，我们证明 SUPER* 接近最优，而流行的基线相当次优。在对来自 ICLR 2018 的真实数据和合成数据的实验中，我们发现 SUPER* 大大优于部署在现有系统中的基线，持续减少了 50-75% 或更多的论文数量，并且对各种现实世界的复杂性。