Query Exposure Prediction For Groups Of Documents In Rankings

Abstract

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,

Stats

• citations3
• S2 citations—
• github stars0
• HF likes0
• heat score4.52
• arxiv keyjaenich2024query

Related papers

• Evaluating Stochastic Rankings With Expected Exposure (2020)15.83
• A Passage-based Approach To Learning To Rank Documents (2019)8.60
• Exposing Query Identification For Search Transparency (2021)6.77
• Drowning In Documents: Consequences Of Scaling Reranker Inference (2024)0.00
• Document Optimization For Black-box Retrieval Via Reinforcement Learning (2026)0.00
• Setrank: Learning A Permutation-invariant Ranking Model For Information Retrieval (2019)0.00
• Neural Ranking Models For Document Retrieval (2021)11.08
• Towards Axiomatic Explanations For Neural Ranking Models (2021)10.35