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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

classification cs.IRcs.DL
keywords historicalrecommendersystemsdigitalhumanitiesresearchstakeholdersvalue
verification ladder T0 review T1 audit T2 compute T3 formal
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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 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 4 citations worldwide. 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. 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.

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