The main objective of an Information Retrieval system is to provide a user with the most relevant documents to the user's query. To do this, modern IR systems typically deploy a re-ranking pipeline in which a set of documents is retrieved by a lightweight first-stage retrieval process and then re-ranked by a more effective but expensive model. However, the success of a re-ranking pipeline is heavily dependent on the performance of the first stage retrieval, since new documents are not usually identified during the re-ranking stage. Moreover, this can impact the amount of exposure that a particular group of documents, such as documents from a particular demographic group, can receive in the final ranking. For example, the fair allocation of exposure becomes more challenging or impossible if the first stage retrieval returns too few documents from certain groups, since the number of group documents in the ranking affects the exposure more than the documents' positions. With this in mind, it is beneficial to predict the amount of exposure that a group of documents is likely to receive in the results of the first stage retrieval process, in order to ensure that there are a sufficient number of documents included from each of the groups. In this paper, we introduce the novel task of query exposure prediction (QEP). Specifically, we propose the first approach for predicting the distribution of exposure that groups of documents will receive for a given query. Our new approach, called GEP, uses lexical information from individual groups of documents to estimate the exposure the groups will receive in a ranking. Our experiments on the TREC 2021 and 2022 Fair Ranking Track test collections show that our proposed GEP approach results in exposure predictions that are up to 40 % more accurate than the predictions of adapted existing query performance prediction and resource allocation approaches.

翻译：信息检索系统的主要目标是为用户提供与其查询最相关的文档。为此，现代信息检索系统通常采用重排序流水线：先通过轻量级初检阶段检索出一组文档，再经更高效但昂贵的模型进行重排序。然而，重排序流水线的效果高度依赖于初检阶段的性能，因为在重排序阶段通常不会识别新文档。此外，这会影响特定文档组（如来自特定人口群体的文档）在最终排序中获得的曝光量。例如，若初检阶段返回的某些群体文档过少，则公平分配曝光量将更具挑战性或不可行——因为排序中群体文档的数量对曝光量的影响显著超过文档位置。基于此，预测某组文档在初检阶段结果中可能获得的曝光量具有重要价值，可确保每个群体都有足够数量的文档被纳入排序。本文提出了查询曝光量预测（QEP）这一新任务。具体而言，我们首次提出预测给定查询下各组文档曝光量分布的方法。新方法GEP利用各组文档的词汇信息估计其在排序中获得的曝光量。在TREC 2021和2022公平排序赛道测试集上的实验表明，相较于适配的现有查询性能预测与资源分配方法，我们提出的GEP方法的预测准确率最高提升40%。