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De-centering the (Traditional) User: Multistakeholder Evaluation of Recommender Systems

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arxiv 2501.05170 v2 pith:2UD6KWU6 submitted 2025-01-09 cs.IR cs.LG

classification cs.IRcs.LG
keywords multistakeholderrecommendersystemsevaluationapplicationsaspectscaseinvolved
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Multistakeholder Approach to Value-Driven Co-Design of Recommender System Evaluation Metrics in Digital Archives

    cs.IR 2025-07 conditional novelty 6.0 of 10

    A qualitative study translates stakeholder values from digital archives focus groups into a four-stage research funnel and eight proposed recommender evaluation metric directions.

  2. Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI Recommendations

    cs.IR 2025-07 conditional novelty 6.0 of 10

    Combining the context-aware LORE model with calibrated popularity re-ranking yields POI recommendations whose popularity distribution most closely matches users' historical preferences.

  3. Multistakeholder Fairness in Tourism: What can Algorithms learn from Tourism Management?

    cs.IR 2025-08 conditional novelty 5.0 of 10

    A comparative literature review shows tourism management and computer science define multistakeholder fairness differently, and argues algorithmic design should adopt qualitative, participatory methods from tourism research.

  4. Recommending With, Not For: Co-Designing Recommender Systems for Social Good

    cs.HC 2025-08 conditional novelty 5.0 of 10

    Recommender systems for social good should be co-designed with the communities they affect, across problem definition, design, evaluation, and monitoring.

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