A deep learning approach for context-aware citation recommendation using rhetorical zone classification and similarity to overcome cold-start problem

Abbas, Muhammad Azeem (2022) A deep learning approach for context-aware citation recommendation using rhetorical zone classification and similarity to overcome cold-start problem. Journal of Ambient Intelligence and Humanized Computing. ISSN 1868-5137

[img]
Preview
 Text
Abbas, Muhammad (2022) A deep learning approach.pdf - Accepted Version
Available under License CC-BY-NC Creative Commons Attribution Non-commercial.
Download (971kB) | Preview

Abstract

In the recent decade, the citation recommendation has emerged as an important research topic due to its need for the huge size of published scientific work. Among other citation recommendation techniques, the widely used content-based filtering (CBF) exploits research articles’ textual content to produce recommendations. However, CBF techniques are prone to the well-known cold-start problem. On the other hand, deep learning has shown its effectiveness in understanding the semantics of the text. The present paper proposes a citation recommendation system using deep learning models to classify rhetorical zones of the research articles and compute similarity using rhetorical zone embeddings that overcome the cold-start problem. Rhetorical zones are the predefined linguistic categories having some common characteristics about the text. A deep learning model is trained using ART and CORE datasets with an accuracy of 76 per cent. The final ranked lists of the recommendations have an average of 0.704 normalized discounted cumulative gain (nDCG) score involving ten domain experts. The proposed system is applicable for both local and global context-aware recommendations.

Item Type: Article
Uncontrolled Keywords: content-based filtering; cold-start; Bi-LSTM;
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Institutes and Academies > Wales Institute for Science & Art (WISA) > Academic Discipline: Applied Computing
Depositing User: Muhammad Azeem Abbas
Date Deposited: 10 May 2022 08:49
Last Modified: 11 Sep 2024 17:03
URI: https://repository.uwtsd.ac.uk/id/eprint/1972

Administrator Actions (login required)

Edit Item - Repository Staff Only Edit Item - Repository Staff Only