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

Abbas, Muhammad; Ajayi, Saheed; Bilal, Muhammad; Oyegoke, Ade; Pasha, Maruf; Tauqeer Ali, Hafiz

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.

Citation

Abbas, M., Ajayi, S., Bilal, M., Oyegoke, A., Pasha, M., & Tauqeer Ali, H. (2024). 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, 15, 419–433. https://doi.org/10.1007/s12652-022-03899-6

• Journal Article Type: Article
• Acceptance Date: May 4, 2022
• Online Publication Date: May 28, 2022
• Publication Date: Jan 31, 2024
• Deposit Date: May 27, 2022
• Publicly Available Date: Apr 11, 2024
• Journal: Journal of Ambient Intelligence and Humanized Computing
• Print ISSN: 1868-5137
• Electronic ISSN: 1868-5145
• Publisher: Springer (part of Springer Nature)
• Peer Reviewed: Peer Reviewed
• Volume: 15
• Pages: 419–433
• DOI: https://doi.org/10.1007/s12652-022-03899-6
• Keywords: Content-based filtering; Cold-start; Bi-LSTM
• Public URL: https://uwe-repository.worktribe.com/output/9572533
• Publisher URL: https://www.springer.com/journal/12652/