Exploiting Citation Knowledge in Personalised Recommendation of Recent Scientific Publications

Khadka, Anita; Cantador, Ivan and Fernandez, Miriam (2020). Exploiting Citation Knowledge in Personalised Recommendation of Recent Scientific Publications. In: LREC 2020, Conference on Language Resources and Evaluation, 11-16 May 2020, Marseille.

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
• 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