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RAR-SB: research article recommendation using SciBERT with BiGRU

Author

Listed:
  • Nimbeshaho Thierry

    (Nanjing University of Posts and Telecommunications)

  • Bing-Kun Bao

    (Nanjing University of Posts and Telecommunications
    Nanjing University of Posts and Telecommunications)

  • Zafar Ali

    (Southeast University)

Abstract

The wide range and enormous volume of academic papers on the Internet prompted researchers to recommend models that could provide users with customized academic article recommendations. Nevertheless, previous approaches struggled with “sparsity” and “cold-start” as a consequence of a lack of sufficient information about research articles. Furthermore, they fail to recognize the importance of important factors and long-range dependencies, thus restricting their ability to make reliable and reasonable recommendations. To address these issues, we suggest RAR-SB, a research article recommender model that uses a pre-trained language model for scientific text named SciBERT to learn context-preserving research article representations. To learn the researcher’s preferences, the model exploits semantics corresponding to the title, abstract, authors, and field of study(FoS)/keywords of the candidate and query papers. The model captures long-range dependencies and salient features using the BiGRU network and the attention module, respectively. The experimental findings on the DBLP-V12 dataset demonstrate that the suggested recommendation model outperforms the baseline approaches regarding mean reciprocal rank (MRR) and mean average precision (MAP) by nearly 3.7% and 5.3%, respectively. Similarly, on the DBLP-V13 dataset, the proposed model has improved 6% and 5% better MRR and MAP results, respectively.

Suggested Citation

  • Nimbeshaho Thierry & Bing-Kun Bao & Zafar Ali, 2023. "RAR-SB: research article recommendation using SciBERT with BiGRU," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(12), pages 6427-6448, December.
  • Handle: RePEc:spr:scient:v:128:y:2023:i:12:d:10.1007_s11192-023-04840-0
    DOI: 10.1007/s11192-023-04840-0
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    References listed on IDEAS

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    1. Chanathip Pornprasit & Xin Liu & Pattararat Kiattipadungkul & Natthawut Kertkeidkachorn & Kyoung-Sook Kim & Thanapon Noraset & Saeed-Ul Hassan & Suppawong Tuarob, 2022. "Enhancing citation recommendation using citation network embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 233-264, January.
    2. Yonghe Lu & Meilu Yuan & Jiaxin Liu & Minghong Chen, 2023. "Research on semantic representation and citation recommendation of scientific papers with multiple semantics fusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1367-1393, February.
    3. Chanwoo Jeong & Sion Jang & Eunjeong Park & Sungchul Choi, 2020. "A context-aware citation recommendation model with BERT and graph convolutional networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1907-1922, September.
    4. Zafar Ali & Guilin Qi & Pavlos Kefalas & Shah Khusro & Inayat Khan & Khan Muhammad, 2022. "SPR-SMN: scientific paper recommendation employing SPECTER with memory network," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6763-6785, November.
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