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A number of applications involve sequential arrival of users, and requireshowing each user an ordering of items. A prime example (which forms the focusof this paper) is the bidding process in conference peer review where reviewersenter the system sequentially, each reviewer needs to be shown the list ofsubmitted papers, and the reviewer then "bids" to review some papers. The orderof the papers shown has a significant impact on the bids due to primacyeffects. In deciding on the ordering of papers to show, there are two competinggoals: (i) obtaining sufficiently many bids for each paper, and (ii) satisfyingreviewers by showing them relevant items. In this paper, we begin by developinga framework to study this problem in a principled manner. We present analgorithm called SUPER*, inspired by the A* algorithm, for this goal.Theoretically, we show a local optimality guarantee of our algorithm and provethat popular baselines are considerably suboptimal. Moreover, under a communitymodel for the similarities, we prove that SUPER* is near-optimal whereas thepopular baselines are considerably suboptimal. In experiments on real data fromICLR 2018 and synthetic data, we find that SUPER* considerably outperformsbaselines deployed in existing systems, consistently reducing the number ofpapers with fewer than requisite bids by 50-75% or more, and is also robust tovarious real world complexities.

A SUPER* Algorithm to Optimize Paper Bidding in Peer Review