Multistakeholder and Multimethod Evaluation

Robin Burke, Gediminas Adomavicius, Toine Bogers, Tomasso Di Noia, Dominik Kowald, Julia Neidhardt, Özlem Özgöbek, Maria Pera, Jürgen Ziegler

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

Abstract

Multistakeholder recommender systems are defined by [1] as those that account for “the preferences of multiple parties when generating recommendations, especially when these parties are on different sides of the recommendation interaction.” Due to their complexity, evaluating these systems cannot be restricted to the overall utility of a single stakeholder, as is often the case of more mainstream recommender system applications.
In this section, we focus our discussion on the intricacies involved in understanding what is the “right” construct required to ensure the proper evaluation of multistakeholder recommender systems. We bring attention to the different aspects involved in the evaluation of multistakeholder recommender systems – from the range of stakeholders involved (beyond producers and consumers) to the values and specific goals of each relevant stakeholder. Additionally, we discuss how to move from theoretical evaluation to practical implementation, providing specific use case examples. Finally, we outline open research directions for the RecSys community to explore. Our aim in this section is to provide guidance to researchers and practitioners about how to think about these complex and domain-dependent issues in the course of designing, developing, and researching applications with multistakeholder aspects.
Original languageEnglish
Title of host publicationEvaluation Perspectives of Recommender Systems: Driving Research and Education (Dagstuhl Seminar 24211)
Volume14
Place of PublicationDagstuhl
PublisherSchloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH
Publication date26 Nov 2024
Edition5
Pages123-145
Publication statusPublished - 26 Nov 2024
SeriesDagstuhl Seminar Proceedings
Number5
Volume14
ISSN1862-4405

Keywords

  • Recommender Systems
  • Evaluation
  • Information Retrieval
  • User Interaction
  • Intelligent Systems
  • Multi-Stakeholder Processes

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