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A novel hybrid publication recommendation system using compound information

  • Qiang Yang
  • Zhixu LiEmail author
  • An Liu
  • Guanfeng Liu
  • Lei Zhao
  • Xiangliang Zhang
  • Min Zhang
  • Xiaofang Zhou
Article
  • 6 Downloads
Part of the following topical collections:
  1. Special Issue on Web and Big Data

Abstract

Publication recommendation is an interesting but challenging research problem. Most existing studies only use partial information of papers’ contents, reference network or co-author relationship, which leads to an unsatisfied recommendation result. In this study, we propose a novel hybrid publication recommendation approach using compound information which retrieves top-K most relevant papers from a publication depository for a set of user input keywords. Our advantages comparing to the existing methods include: (1) Reaching a better recommendation results by taking the advantages of both content-based recommendation and citation-based recommendation and exploring much richer information of papers in one method; (2) Effectively solving the cold-start problem for new published papers by considering the vitality of papers and the impact factor of venues into the citation network; (3) Saving a large overhead in calculating the content-based similarity between papers and user input keywords by doing paper clustering based on the citation network. Extensive experiments on DBLP and Microsoft Academic datasets demonstrate that PubTeller improves the state-of-the-art methods with 4% in Precision and 4.5% in Recall.

Keywords

Publication recommendation Compound information Edge-reinforced citation network Citation network cluster 

Notes

Acknowledgements

This research is partially supported by National Natural Science Foundation of China (No. 61632016, 61572336, 61572335, 61772356), the Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003, 18KJA520010), the Open Program of Neusoft Corporation (No. SKLSAOP1801), and King Abdullah University of Science and Technology (KAUST) under award number FCC/1/1976-19-01.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Qiang Yang
    • 1
    • 2
  • Zhixu Li
    • 1
    • 3
    Email author
  • An Liu
    • 1
  • Guanfeng Liu
    • 1
    • 4
  • Lei Zhao
    • 1
  • Xiangliang Zhang
    • 2
  • Min Zhang
    • 1
  • Xiaofang Zhou
    • 1
    • 5
  1. 1.Institute of Artificial Intelligence, School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.King Abdullah University of Science and TechnologyJeddahSaudi Arabia
  3. 3.IFLYTEK ResearchSuzhouChina
  4. 4.Department of ComputingMacquarie UniversitySydneyAustralia
  5. 5.The University of QueenslandBrisbaneAustralia

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