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De-centering the (Traditional) user: Multistakeholder evaluation of recommender systems

  • Robin Burke
  • , Gediminas Adomavicius
  • , Toine Bogers
  • , Tommaso Di Noia
  • , Dominik Kowald
  • , Julia Neidhardt
  • , Özlem Özgöbek
  • , Maria Soledad Pera
  • , Nava Tintarev
  • , Jürgen Ziegler
  • Norwegian University of Science and Technology
  • Delft University of Technology
  • Maastricht University

Research output: Journal Article or Conference Article in JournalJournal articleResearchpeer-review

Abstract

Multistakeholder recommender systems are those that account for the impacts and preferences of multiple groups of individuals, not just the end users receiving recommendations. Due to their complexity, these systems cannot be evaluated strictly by the overall utility of a single stakeholder, as is often the case of more mainstream recommender system applications. In this article, we focus our discussion on the challenges of multistakeholder evaluation of recommender systems. We bring attention to the different aspects involved—from the range of stakeholders involved (including but not limited to providers and consumers) to the values and specific goals of each relevant stakeholder. We discuss how to move from theoretical principles to practical implementation, providing specific use case examples. Finally, we outline open research directions for the RecSys community to explore. We aim to provide guidance to researchers and practitioners about incorporating these complex and domain-dependent issues of evaluation in the course of designing, developing, and researching applications with multistakeholder aspects.
Original languageEnglish
JournalInternational Journal of Human-Computer Studies
Volume203
Issue number103560
ISSN1071-5819
DOIs
Publication statusPublished - Sept 2025

Keywords

  • recommender systems
  • multi-stakeholder recommendation
  • multi-stakeholder processes
  • music recommendation
  • job recommendation
  • technology-enhanced learning
  • evaluation

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  • Multistakeholder and Multimethod Evaluation

    Burke, R., Adomavicius, G., Bogers, T., Di Noia, T., Kowald, D., Neidhardt, J., Özgöbek, Ö., Pera, M. & Ziegler, J., 26 Nov 2024, Evaluation Perspectives of Recommender Systems: Driving Research and Education (Dagstuhl Seminar 24211). 5 ed. Dagstuhl: Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Vol. 14. p. 123-145 (Dagstuhl Seminar Proceedings; No. 5, Vol. 14).

    Research output: Conference Article in Proceeding or Book/Report chapterReport chapterResearchpeer-review

    Open Access

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