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A scientific paper recommendation method using the time decay heterogeneous graph

Author

Listed:
  • Zhenye Huang

    (South China University of Technology)

  • Deyou Tang

    (South China University of Technology)

  • Rong Zhao

    (South China University of Technology)

  • Wenjing Rao

    (South China University of Technology)

Abstract

Finding appropriate and relevant papers about a project in various digital libraries with millions of scientific papers is challenging for researchers, resulting in a research innovation gap because of incomplete literature retrieval. A query-oriented paper recommendation (QPR) is a feasible way to improve the efficiency of literature retrieval in scientific research, and the graph-based method is one of the best solutions for QPR. However, current graph-based QPR methods still have the defeats of low precision and over-weighting. This paper proposes a query-oriented paper recommendation method using the Time Decay Heterogeneous Graph (TDHG) to improve the recommendation quality. TDHG is a four-layer heterogeneous graph combing the time decay characteristics in academic literature. We also used author rank to highlight contributions, the Author-Topic model to extend relations in heterogeneous graphs, and the Random Walk with Restart algorithm to rank papers. We designed three time-decay vectors and compared their impact on overcoming over-weighting caused by applying Random Walk with Restart algorithm to the original heterogeneous graph. Our experiments show linear time-decay vectors cannot balance the importance and timeliness of academic papers, while log time-decay vectors and sqrt time-decay vectors effectively solve the over-weighting problem. The experimental results show that the time-decay vector brings about an 11% and 8% improvement in Mean Average Precision (MAP) on the AAN and DBLP datasets, respectively.

Suggested Citation

  • Zhenye Huang & Deyou Tang & Rong Zhao & Wenjing Rao, 2024. "A scientific paper recommendation method using the time decay heterogeneous graph," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(3), pages 1589-1613, March.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:3:d:10.1007_s11192-024-04933-4
    DOI: 10.1007/s11192-024-04933-4
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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. Zafar Ali & Irfan Ullah & Amin Khan & Asim Ullah Jan & Khan Muhammad, 2021. "An overview and evaluation of citation recommendation models," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4083-4119, May.
    3. Zafar Ali & Irfan Ullah & Amin Ul Haq & Asim Ullah Jan & Khan Muhammad, 2021. "Correction to: An overview and evaluation of citation recommendation models," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8771-8771, October.
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