Making Ukiyo-e Easier to Discover: A Recommender System for Digital Archives

poster / demo / art installation
Authorship
  1. 1. Jiayun Wang

    Graduate School of Information Science and Engineering - Ritsumeikan University

  2. 2. Biligsaikhan Batjargal

    Kinugasa Research Organization - Ritsumeikan University

  3. 3. Akira Maeda

    College of Information Science and Engineering - Ritsumeikan University

  4. 4. Kyoji Kawagoe

    College of Information Science and Engineering - Ritsumeikan University

  5. 5. Ryo Akama

    School of Letters - Ritsumeikan University

Work text
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Ukiyo-e is a kind of woodblock print that has high artistic and research value. It is preserved by many digital archives (DAs), such as the Art Research Center Ukiyo-e Portal Database (ARC-UDB) of Ritsumeikan University. ARC-UDB is mainly built for the experts of humanities fields. In this research, to meet the potential needs of the expert users who will browse or explore ARC-UDB, we propose a recommender system. The proposed recommender system utilizes an existing link prediction model, which exploits the graph-like datasets of ARC-UDB as the input of the recommendation algorithm. We optimize the format of input of the link prediction model, to the format that is suitable for ARC-UDB datasets. From the results, we find that the proposed method is effective for the task. This recommender system could also be applied to other DAs that are with graph-like dataset structures.

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Conference Info

In review

ADHO - 2020
"carrefours / intersections"

Hosted at Carleton University, Université d'Ottawa (University of Ottawa)

Ottawa, Ontario, Canada

July 20, 2020 - July 25, 2020

475 works by 1078 authors indexed

Conference cancelled due to coronavirus. Online conference held at https://hcommons.org/groups/dh2020/. Data for this conference were initially prepared and cleaned by May Ning.

Conference website: https://dh2020.adho.org/

References: https://dh2020.adho.org/abstracts/

Series: ADHO (15)

Organizers: ADHO