Record link: 
http://hdl.handle.net/20.500.12708/224592
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Title: 
LongEval at CLEF 2025: Longitudinal Evaluation of IR Model Performance
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Citation: 
Cancellieri, M., El-Ebshihy, A., Fink, T., Galuščáková, P., Gonzalez-Saez, G., Goeuriot, L., Iommi, D., Keller, J., Knoth, P., Mulhem, P., Piroi, F., Pride, D., & Schaer, P. (2025). LongEval at CLEF 2025: Longitudinal Evaluation of IR Model Performance. In C. Hauff, C. Macdonald, D. Jannach, G. Kazai, F. M. Nardini, F. Pinelli, F. Silvestri, & N. Tonellotto (Eds.), Advances in Information Retrieval : 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6–10, 2025, Proceedings, Part V (pp. 382–388). Springer. https://doi.org/10.1007/978-3-031-88720-8_58
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Publisher DOI: 
10.1007/978-3-031-88720-8_58
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Cancellieri, Matteo 
El-Ebshihy, Alaa 
Fink, Tobias 
Galuščáková, Petra 
Gonzalez-Saez, Gabriela 
Goeuriot, Lorraine 
Iommi, David 
Keller, Jüri 
Knoth, Petr 
Mulhem, Philippe 
Piroi, Florina 
Pride, David 
Schaer, Philipp 
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Organisational Unit: 
E194-04 - Forschungsbereich Data Science 
E058-06 - Fachbereich Zentrum für Forschungsdatenmanagement 
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Published in: 
Advances in Information Retrieval : 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6–10, 2025, Proceedings, Part V 
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ISBN: 
978-3-031-88720-8
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Volume: 
15576
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DOI of the book: 
10.1007/978-3-031-88720-8
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Date (published): 
2025
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Event name: 
47th European Conference on Information Retrieval 
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Event date: 
6-Apr-2025 - 10-Apr-2025
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Event place: 
Lucca, Italy
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Number of Pages: 
7
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Publisher: 
Springer, Cham
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Peer reviewed: 
Yes
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Keywords: 
Information Retrieval; Longitudinal Evaluation; Temporal Change; Temporal Generalisability; Temporal Persistence
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Abstract: 
This paper presents the third edition of the LongEval Lab, part of the CLEF 2025 conference, which continues to explore the challenges of temporal persistence in Information Retrieval (IR). The lab features two tasks designed to provide researchers with test data that reflect the evolving nature of user queries and document relevance over time. By evaluating how model performance degrades as test data diverge temporally from training data, LongEval seeks to advance the understanding of temporal dynamics in IR systems. The 2025 edition aims to engage the IR and NLP communities in addressing the development of adaptive models that can maintain retrieval quality over time in the domains of web search and scientific retrieval.
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Research Areas: 
Information Systems Engineering: 100%
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Science Branch: 
1020 - Informatik: 90%
5020 - Wirtschaftswissenschaften: 10%
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Appears in Collections:
Conference Paper

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