Academic information retrieval using citation clusters: in-depth evaluation based on systematic reviews

Juan Pablo Bascur (), Suzan Verberne, Nees Jan Eck and Ludo Waltman
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Juan Pablo Bascur: Leiden University
Suzan Verberne: Leiden University
Nees Jan Eck: Leiden University
Ludo Waltman: Leiden University

Scientometrics, 2023, vol. 128, issue 5, No 13, 2895-2921

Abstract: Abstract The field of science mapping has shown the power of citation-based clusters for literature analysis, yet this technique has barely been used for information retrieval tasks. This work evaluates the performance of citation-based clusters for information retrieval tasks. We simulated a search process with a tree hierarchy of clusters and a cluster selection algorithm. We evaluated the task of finding the relevant documents for 25 systematic reviews. Our evaluation considered several trade-offs between recall and precision for the cluster selection. We also replicated the Boolean queries self-reported by the systematic reviews to serve as a reference. We found that citation-based clusters’ search performance is highly variable and unpredictable, that the clusters work best for users that prefer recall over precision at a ratio between 2 and 8, and that the clusters are able to complement query-based search by finding additional relevant documents.

Keywords: Information retrieval; Citation-based clustering; Systematic reviews; Evaluation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11192-023-04681-x

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