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Value Identification in Multistakeholder Recommender Systems for Humanities and Historical Research: The Case of the Digital Archive Monasterium.net

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arxiv 2409.17769 v1 pith:TELXZMCJ submitted 2024-09-26 cs.IR cs.DL

Value Identification in Multistakeholder Recommender Systems for Humanities and Historical Research: The Case of the Digital Archive Monasterium.net

classification cs.IR cs.DL
keywords historicalrecommendersystemsdigitalhumanitiesresearchstakeholdersvalue
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Recommender systems remain underutilized in humanities and historical research, despite their potential to enhance the discovery of cultural records. This paper offers an initial value identification of the multiple stakeholders that might be impacted by recommendations in Monasterium.net, a digital archive for historical legal documents. Specifically, we discuss the diverse values and objectives of its stakeholders, such as editors, aggregators, platform owners, researchers, publishers, and funding agencies. These in-depth insights into the potentially conflicting values of stakeholder groups allow designing and adapting recommender systems to enhance their usefulness for humanities and historical research. Additionally, our findings will support deeper engagement with additional stakeholders to refine value models and evaluation metrics for recommender systems in the given domains. Our conclusions are embedded in and applicable to other digital archives and a broader cultural heritage context.

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Cited by 3 Pith papers

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

  1. What Do Humanities Scholars Need? A User Model for Recommendation in Digital Archives

    cs.DL 2026-04 unverdicted novelty 6.0

    Humanities scholars require recommender user models for digital archives that account for context volatility, epistemic trust, contrastive seeking, and strand continuity instead of stable preferences and session-bound...

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

    cs.IR 2025-08 conditional novelty 5.0

    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.

  3. Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems

    cs.IR 2026-05 unverdicted novelty 4.0

    The authors propose a conceptual framework integrating stakeholder-LLM alignment methods, social choice-based aggregation for collective decisions, and stakeholder-centric evaluations to achieve fair multi-agent perso...