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Chronological citation recommendation with time preference

Author

Listed:
  • Shutian Ma

    (Nanjing University of Science and Technology)

  • Heng Zhang

    (Nanjing University of Science and Technology)

  • Chengzhi Zhang

    (Nanjing University of Science and Technology)

  • Xiaozhong Liu

    (Indiana University)

Abstract

Citation recommendation is an important task to assist scholars in finding candidate literature to cite. Traditional studies focus on static models of recommending citations, which do not explicitly distinguish differences between papers that are caused by temporal variations. Although, some researchers have investigated chronological citation recommendation by adding time related function or modeling textual topics dynamically. These solutions can hardly cope with function generalization or cold-start problems when there is no information for user profiling or there are isolated papers never being cited. With the rise and fall of science paradigms, scientific topics tend to change and evolve over time. People would have the time preference when citing papers, since most of the theoretical basis exist in classical readings that published in old time, while new techniques are proposed in more recent papers. To explore chronological citation recommendation, this paper wants to predict the time preference based on user queries, which is a probability distribution of citing papers published in different time slices. Then, we use this time preference to re-rank the initial citation list obtained by content-based filtering. Experimental results demonstrate that task performance can be further enhanced by time preference and it’s flexible to be added in other citation recommendation frameworks.

Suggested Citation

  • Shutian Ma & Heng Zhang & Chengzhi Zhang & Xiaozhong Liu, 2021. "Chronological citation recommendation with time preference," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2991-3010, April.
  • Handle: RePEc:spr:scient:v:126:y:2021:i:4:d:10.1007_s11192-021-03878-2
    DOI: 10.1007/s11192-021-03878-2
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    References listed on IDEAS

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    1. Lutz Bornmann & Rüdiger Mutz, 2015. "Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2215-2222, November.
    2. Shutian Ma & Chengzhi Zhang & Xiaozhong Liu, 2020. "A review of citation recommendation: from textual content to enriched context," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1445-1472, March.
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    Citations

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    Cited by:

    1. Chi Jiang & Xiao Ma & Jiangfeng Zeng & Yin Zhang & Tingting Yang & Qiumiao Deng, 2023. "TAPRec: time-aware paper recommendation via the modeling of researchers’ dynamic preferences," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3453-3471, June.
    2. Shicheng Tan & Tao Zhang & Shu Zhao & Yanping Zhang, 2023. "Self-supervised scientific document recommendation based on contrastive learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5027-5049, September.
    3. 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.
    4. Hei-Chia Wang & Jen-Wei Cheng & Che-Tsung Yang, 2022. "SentCite: a sentence-level citation recommender based on the salient similarity among multiple segments," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2521-2546, May.

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