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The field of scientometrics has shown the power of citation-based clustersfor literature analysis, yet this technique has barely been used forinformation retrieval tasks. This work evaluates the performance of citationbased-clusters for information retrieval tasks. We simulated a search processusing these clusters with a tree hierarchy of clusters and a cluster selectionalgorithm. We evaluated the task of finding the relevant documents for 25systematic reviews. Our evaluation considered several trade-offs between recalland precision for the cluster selection, and we also replicated the Booleanqueries self-reported by the systematic review to serve as a reference. Wefound that citation-based clusters search performance is highly variable andunpredictable, that it works best for users that prefer recall over precisionat a ratio between 2 and 8, and that when used along with query-based searchthey complement each other, including finding new relevant documents.

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