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A Recommender System for EOSC. Challenges and Possible Solutions

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Research Challenges in Information Science (RCIS 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 446))

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Abstract

European Open Science Cloud (EOSC) is a pan-European environment providing researchers with a plethora or publicly-available, open resources and services to help them conduct their research. Availability of publications, datasets, computational power, networks or storage allows researchers to concentrate on their research rather than the technical infrastructures. However, the plenitude and diversity of items offered in EOSC increases and becomes overwhelming for researchers who expect guidance and support. Recommender systems allow them to assign rankings to subject object, based on their value for specific end users, inferred from diverse data about them, their behaviour or various relationships between users and objects. In this paper we present architectural and functional challenges related to the EOSC Recommender System that could substantially improve the experience of researchers using EOSC offerings.

Supported by the EOSC Future project, which is co-funded by the European Union Horizon 2020 Programme call INFRAEOSC-03-2020 – Grant Agreement Number 101017536.

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Notes

  1. 1.

    https://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers.

  2. 2.

    https://scholar.google.com.

  3. 3.

    https://eosc-portal.eu/news/esfri-clusters-and-thematic-clouds-share-their-positions-eosc.

  4. 4.

    https://www.blue-cloud.org/.

  5. 5.

    https://eosc-portal.eu/sites/default/files/EOSC-Enhance-WP2-JNP-D2.4-2020-11-26_v1.pdf.

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Authors and Affiliations

  1. Poznań Supercomputing and Networking Center, Poznań, Poland

    Marcin Wolski, Krzysztof Martyn & Bartosz Walter

  2. Poznań University of Technology, Poznań, Poland

    Bartosz Walter

Authors
  1. Marcin Wolski
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  2. Krzysztof Martyn
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  3. Bartosz Walter
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Corresponding author

Correspondence to Marcin Wolski .

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Editors and Affiliations

  1. University of Twente, Enschede, The Netherlands

    Renata Guizzardi

  2. University of Geneva, CUI, Carouge, Switzerland

    Jolita Ralyté

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Xavier Franch

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Wolski, M., Martyn, K., Walter, B. (2022). A Recommender System for EOSC. Challenges and Possible Solutions. In: Guizzardi, R., Ralyté, J., Franch, X. (eds) Research Challenges in Information Science. RCIS 2022. Lecture Notes in Business Information Processing, vol 446. Springer, Cham. https://doi.org/10.1007/978-3-031-05760-1_5

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  • DOI: https://doi.org/10.1007/978-3-031-05760-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05759-5

  • Online ISBN: 978-3-031-05760-1

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