De-centering the (Traditional) User: Multistakeholder Evaluation of Recommender Systems
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Jan 2025.
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Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Latercited by 1
- 2025Dagstuhl Perspectives Workshop 24352 -- Conversational Agents: A Framework for Evaluation (CAFE): Manifestocited by 0
- 2026Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systemscited by 0
- 2026CoWeaver: A Bi-directional, Learnable and Explainable Matching Engine for Mixed Human-Agent Science Collaborationcited by 0
- 2026CoWeaver: A Bi-directional, Learnable and Explainable Matching Engine for Mixed Human-Agent Science Collaboration17 Jul 2026
- 2026Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems4 May 2026
- 2025We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later11 Sep 2025
- 2025Dagstuhl Perspectives Workshop 24352 -- Conversational Agents: A Framework for Evaluation (CAFE): Manifesto8 Jun 2025