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Khadka, Anita; Cantador, Ivan and Fernandez, Miriam
(2020).
URL: https://lrec2020.lrec-conf.org/en/
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 information retrieval and filtering.
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About
- Item ORO ID
- 70088
- Item Type
- Conference or Workshop Item
- Extra Information
- Due to the outbreak of Covid-19, LREC 2020 is not going to happen in Marseille, however, a proceeding containing accepted papers will be printed.
- Keywords
- Research publication dataset; citation context; citation types; recommender systems
- Academic Unit or School
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Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Research Group
- Centre for Research in Computing (CRC)
- Depositing User
- Anita Khadka