A deep-learning based citation count prediction model with paper metadata semantic features
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DOI: 10.1007/s11192-021-04033-7
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- Li, Xin & Tang, Xuli & Cheng, Qikai, 2022. "Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network," Journal of Informetrics, Elsevier, vol. 16(4).
- Kayvan Kousha & Mike Thelwall, 2024. "Factors associating with or predicting more cited or higher quality journal articles: An Annual Review of Information Science and Technology (ARIST) paper," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 75(3), pages 215-244, March.
- Fang Zhang & Shengli Wu, 2024. "Predicting citation impact of academic papers across research areas using multiple models and early citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4137-4166, July.
- Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
- Porwal, Priya & Devare, Manoj H., 2024. "Scientific impact analysis: Unraveling the link between linguistic properties and citations," Journal of Informetrics, Elsevier, vol. 18(3).
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Keywords
Citation count prediction; Metadata semantic features; Deep learning; Sentence embedding; Semantic information;All these keywords.
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