<title type="main">Making Ukiyo-e Easier to DiscoverA Recommender System for Digital ArchivesWangJiayunGraduate School of Information Science and Engineering, Ritsumeikan University, Japanjiayunwong@hotmail.comBatjargalBiligsaikhanKinugasa Research Organization, Ritsumeikan University, Japanbiligsaikhan@gmail.comMaedaAkiraCollege of Information Science and Engineering, Ritsumeikan University, Japanamaeda@is.ritsumei.ac.jpKawagoeKyojiCollege of Information Science and Engineering, Ritsumeikan University, Japankawagoe@is.ritsumei.ac.jpAkamaRyoCollege of Letters, Ritsumeikan University, Japanrat03102@lt.ritsumei.ac.jp43921Name, Institution
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PaperPosterDigital ArchiveUkiyo-eRecommender SystemAsiaEnglishContemporaryartificial intelligence and machine learninginformation retrieval and querying algorithms and methodsComputer scienceLibrary & information science

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.