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arXiv · 2509.17469

LongEval at CLEF 2025: Longitudinal Evaluation of IR Systems on Web and Scientific Data

Matteo Cancellieri·Alaa El-Ebshihy·Tobias Fink·Maik Fröbe·Petra Galuščáková·Gabriela Gonzalez-Saez·Lorraine Goeuriot·David Iommi·Jüri Keller·Petr Knoth·Philippe Mulhem·Florina Piroi·David Pride·Philipp Schaer
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Abstract

The LongEval lab focuses on the evaluation of information retrieval systems over time. Two datasets are provided that capture evolving search scenarios with changing documents, queries, and relevance assessments. Systems are assessed from a temporal perspective-that is, evaluating retrieval effectiveness as the data they operate on changes. In its third edition, LongEval featured two retrieval tasks: one in the area of ad-hoc web retrieval, and another focusing on scientific article retrieval. We present an overview of this year's tasks and datasets, as well as the participating systems. A total of 19 teams submitted their approaches, which we evaluated using nDCG and a variety of measures that quantify changes in retrieval effectiveness over time.

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Matteo Cancellieri, Alaa El-Ebshihy, Tobias Fink, Maik Fröbe, Petra Galuščáková, Gabriela Gonzalez-Saez, Lorraine Goeuriot, David Iommi, Jüri Keller, Petr Knoth, Philippe Mulhem, Florina Piroi, David Pride, Philipp Schaer. 2025-09-22. LongEval at CLEF 2025: Longitudinal Evaluation of IR Systems on Web and Scientific Data. https://doi.org/10.1007/978-3-032-04354-2_20

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