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Content-Based Hybrid Deep Neural Network Citation Recommendation Method

  • Conference paper
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Data Science (ICPCSEE 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1258))

Abstract

The rapid growth of scientific papers makes it difficult to query related papers efficiently, accurately and with high coverage. Traditional citation recommendation algorithms rely heavily on the metadata of query documents, which leads to the low quality of recommendation results. In this paper, DeepCite, a content-based hybrid neural network citation recommendation method is proposed. First, the BERT model was used to extract the high-level semantic representation vectors in the text, then the multi-scale CNN model and BiLSTM model were used to obtain the local information and the sequence information of the context in the sentence, and the text vectors were matched in depth to generate candidate sets. Further, the depth neural network was used to rerank the candidate sets by combining the score of candidate sets and multi-source features. In the reranking stage, a variety of Metapath features were extracted from the citation network, and added to the deep neural network to learn, and the ranking of recommendation results were optimized. Compared with PWFC, ClusCite, BM25, RW, NNRank models, the results of the Deepcite algorithm presented in the ANN datasets show that the precision (P@20), recall rate (R@20), MRR and MAP indexesrise by 2.3%, 3.9%, 2.4% and 2.1% respectively. Experimental results on DBLP datasets show that the improvement is 2.4%, 4.3%, 1.8% and 1.2% respectively. Therefore, the algorithm proposed in this paper effectively improves the quality of citation recommendation.

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Notes

  1. 1.

    http://clair.eecs.umich.edu/ann/.

  2. 2.

    http://dblp.uni-trier.de/db/.

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Acknowledgment

The research work is supported by “Shenzhen Science and Technology Project” (JCYJ20180306170836595); “National key research and development program in China” (2019YFB2102300); “the World-Class Universities (Disciplines) and the Characteristic Development Guidance Funds for the Central Universities of China” (PY3A022); “Ministry of Education Fund Projects” (No. 18JZD022 and 2017B00030); “Basic Scientific Research Operating Expenses of Central Universities” (No. ZDYF2017006); “Xi’an Navinfo Corp.& Engineering Center of Xi’an Intelligence Spatial-temporal Data Analysis Project” (C2020103); “Beilin District of Xi’an Science & Technology Project” (GX1803).

Author information

Authors and Affiliations

  1. Shenzhen Research Institute of Xi’an Jiaotong University, Shenzhen, 518057, China

    Leipeng Wang, Yuan Rao, Qinyu Bian & Shuo Wang

  2. Lab of Social Intelligence and Complex Data Processing, Software School, Xi’an Jiaotong University, Xi’an, 710049, China

    Leipeng Wang, Yuan Rao, Qinyu Bian & Shuo Wang

  3. Shanxi Joint Key Laboratory for Artifact Intelligence (Sub-Lab of Xi’an Jiaotong University), Xi’an, 710049, China

    Leipeng Wang, Yuan Rao, Qinyu Bian & Shuo Wang

Authors
  1. Leipeng Wang
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  2. Yuan Rao
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  3. Qinyu Bian
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  4. Shuo Wang
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Corresponding author

Correspondence to Yuan Rao .

Editor information

Editors and Affiliations

  1. North University of China, Taiyuan, China

    Pinle Qin

  2. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  3. Harbin University of Science and Technology, Harbin, China

    Guanglu Sun

  4. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Wang, L., Rao, Y., Bian, Q., Wang, S. (2020). Content-Based Hybrid Deep Neural Network Citation Recommendation Method. In: Qin, P., Wang, H., Sun, G., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1258. Springer, Singapore. https://doi.org/10.1007/978-981-15-7984-4_1

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  • DOI: https://doi.org/10.1007/978-981-15-7984-4_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7983-7

  • Online ISBN: 978-981-15-7984-4

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