Citation Count Prediction Based on Neural Hawkes Model

DOI Web Site 参考文献23件
  • LIU Lisha
    College of Computer Science and Technology, Hangzhou Dianzi University Integrated Graduate School of Medicine, Engineering, and Agricultural Sciences, Faculty of Engineering, University of Yamanashi
  • YU Dongjin
    College of Computer Science and Technology, Hangzhou Dianzi University
  • WANG Dongjing
    College of Computer Science and Technology, Hangzhou Dianzi University
  • FUKUMOTO Fumiyo
    Integrated Graduate School of Medicine, Engineering, and Agricultural Sciences, Faculty of Engineering, University of Yamanashi

抄録

<p>With the rapid development of scientific research, the number of publications, such as scientific papers and patents, has grown rapidly. It becomes increasingly important to identify those with high quality and great impact from such a large volume of publications. Citation count is one of the well-known indicators of the future impact of the publications. However, how to interpret a large number of uncertain factors of publications as relevant features and utilize them to capture the impact of publications over time is still a challenging problem. This paper presents an approach that effectively leverages a variety of factors with a neural-based citation prediction model. Specifically, the proposed model is based on the Neural Hawkes Process (NHP) with the continuous-time Long Short-Term Memory (cLSTM), which can capture the aging effect and the phenomenon of sleeping beauty more effectively from publication covariates as well as citation counts. The experimental results on two datasets show that the proposed approach outperforms the state-of-the-art baselines. In addition, the contribution of covariates to performance improvement is also verified.</p>

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