Optimizing Compound Retrieval Systems

Modern retrieval systems do not rely on a single ranking model to construct their rankings. Instead, they generally take a cascading approach where a sequence of ranking models are applied in multiple re-ranking stages. Thereby, they balance the quality of the top-K ranking with computational costs by limiting the number of documents each model re-ranks. However, the cascading approach is not the only way models can interact to form a retrieval system.

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Publication Details

Published
2025-07-13
DOI
https://doi.org/10.1145/3726302.3730051
Primary Topic
Information Retrieval and Search Behavior
Type
article
Field-Weighted Citation Impact
0.00
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article

Optimizing Compound Retrieval Systems

Harrie Oosterhuis, Rolf Jagerman, Xuanhui Wang, Zhen Qin
Information Retrieval and Search Behavior
article

Optimizing Compound Retrieval Systems

Harrie Oosterhuis, Rolf Jagerman, Xuanhui Wang, Zhen Qin
article en

Abstract

Modern retrieval systems do not rely on a single ranking model to construct their rankings. Instead, they generally take a cascading approach where a sequence of ranking models are applied in multiple re-ranking stages. Thereby, they balance the quality of the top-K ranking with computational costs by limiting the number of documents each model re-ranks. However, the cascading approach is not the only way models can interact to form a retrieval system.

Google (United States) (US), Radboud University Nijmegen (NL)
Openalex Percentile: Top 75%
Information Retrieval and Search Behavior
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