Exploiting Citation Knowledge in Personalised Recommendation of Recent Scientific Publications
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
The Open UniversityDate
2020-05Citation
Proceedings of the Twelfth Language Resources and Evaluation Conference. LREC, 2020Editor's Version
http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.272.pdfSubjects
Research publication dataset; Citation types; Citation context; InformáticaRights
© European Language Resources Association (ELRA)Abstract
In this paper we address the problem of providing personalised recommendations of recent scientific publications to a particular user, and explore the use of citation knowledge to do so. For this purpose, we have generated a novel dataset that captures authors’ publication history and is enriched with different forms of paper citation knowledge, namely citation graphs, citation positions, citation contexts, and citation types. Through a number of empirical experiments on such dataset, we show that the exploitation of the extracted knowledge, particularly the type of citation, is a promising approach for recommending recently published papers that may not be cited yet. The dataset, which we make publicly available, also represents a valuable resource for further investigation on academic
nformation retrieval and filtering
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Google Scholar:Khadka, Anita
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Cantador Gutiérrez, Iván
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Fernández, Míriam
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