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Evaluating Research Dataset Recommendations in a Living Lab

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Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13390))

Abstract

The search for research datasets is as important as laborious. Due to the importance of the choice of research data in further research, this decision must be made carefully. Additionally, because of the growing amounts of data in almost all areas, research data is already a central artifact in empirical sciences. Consequentially, research dataset recommendations can beneficially supplement scientific publication searches. We formulated the recommendation task as a retrieval problem by focussing on broad similarities between research datasets and scientific publications. In a multistage approach, initial recommendations were retrieved by the BM25 ranking function and dynamic queries. Subsequently, the initial ranking was re-ranked utilizing click feedback and document embeddings. The proposed system was evaluated live on real user interaction data using the STELLA infrastructure in the LiLAS Lab at CLEF 2021. Our experimental system could efficiently be fine-tuned before the live evaluation by pre-testing the system with a pseudo test collection based on prior user interaction data from the live system. The results indicate that the experimental system outperforms the other participating systems.

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Notes

  1. 1.

    https://ir.nist.gov/covidSubmit/index.html.

  2. 2.

    https://search.gesis.org/.

  3. 3.

    https://search.gesis.org/.

  4. 4.

    https://pypi.org/project/deep-translator/.

  5. 5.

    https://www.cessda.eu/.

  6. 6.

    https://github.com/allenai/paper-embedding-public-apis.

  7. 7.

    https://github.com/cvangysel/pytrec_eval.

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Authors and Affiliations

  1. Technische Hochschule Köln, Ubierring 48, 50678, Cologne, Germany

    Jüri Keller & Leon Paul Mondrian Munz

Authors
  1. Jüri Keller
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  2. Leon Paul Mondrian Munz
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Correspondence to Jüri Keller .

Editor information

Editors and Affiliations

  1. University of Bologna, Forlì, Italy

    Alberto Barrón-Cedeño

  2. University of Padua, Padova, Italy

    Giovanni Da San Martino

  3. University of Bologna, Bologna, Italy

    Mirko Degli Esposti

  4. Instituto di Scienza e Tecnologie dell' Informazione “Alessandro Faedo”, Pisa, Italy

    Fabrizio Sebastiani

  5. University of Glasgow, Glasgow, UK

    Craig Macdonald

  6. University Milano-Bicocca, Milan, Italy

    Gabriella Pasi

  7. TU Wien, Vienna, Austria

    Allan Hanbury

  8. Leipzig University, Leipzig, Germany

    Martin Potthast

  9. University of Padua, Padova, Italy

    Guglielmo Faggioli

  10. University of Padua, Padova, Italy

    Nicola Ferro

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Keller, J., Munz, L.P.M. (2022). Evaluating Research Dataset Recommendations in a Living Lab. In: Barrón-Cedeño, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2022. Lecture Notes in Computer Science, vol 13390. Springer, Cham. https://doi.org/10.1007/978-3-031-13643-6_11

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  • DOI: https://doi.org/10.1007/978-3-031-13643-6_11

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