LongEval at CLEF 2025: Longitudinal Evaluation of IR Systems on Web and Scientific Data
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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908552105295872 |
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| author | Cancellieri, Matteo El-Ebshihy, Alaa Fink, Tobias Fröbe, Maik Galuščáková, Petra Gonzalez-Saez, Gabriela Goeuriot, Lorraine Iommi, David Keller, Jüri Knoth, Petr Mulhem, Philippe Piroi, Florina Pride, David Schaer, Philipp |
| author_facet | Cancellieri, Matteo El-Ebshihy, Alaa Fink, Tobias Fröbe, Maik Galuščáková, Petra Gonzalez-Saez, Gabriela Goeuriot, Lorraine Iommi, David Keller, Jüri Knoth, Petr Mulhem, Philippe Piroi, Florina Pride, David Schaer, Philipp |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17469 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LongEval at CLEF 2025: Longitudinal Evaluation of IR Systems on Web and Scientific Data Cancellieri, Matteo El-Ebshihy, Alaa Fink, Tobias Fröbe, Maik Galuščáková, Petra Gonzalez-Saez, Gabriela Goeuriot, Lorraine Iommi, David Keller, Jüri Knoth, Petr Mulhem, Philippe Piroi, Florina Pride, David Schaer, Philipp Information Retrieval 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. |
| title | LongEval at CLEF 2025: Longitudinal Evaluation of IR Systems on Web and Scientific Data |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2509.17469 |