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A novel hybrid publication recommendation system using compound information

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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.

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Notes

  1. http://scholar.google.com

  2. https://en.wikipedia.org/wiki/Impact_factor

  3. http://arnetminer.org/DBLP_Citation

  4. http://academic.research.microsoft.com

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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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Authors and Affiliations

  1. Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, Suzhou, China

    Qiang Yang, Zhixu Li, An Liu, Guanfeng Liu, Lei Zhao, Min Zhang & Xiaofang Zhou

  2. King Abdullah University of Science and Technology, Jeddah, Saudi Arabia

    Qiang Yang & Xiangliang Zhang

  3. IFLYTEK Research, Suzhou, China

    Zhixu Li

  4. Department of Computing, Macquarie University, Sydney, Australia

    Guanfeng Liu

  5. The University of Queensland, Brisbane, Australia

    Xiaofang Zhou

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  1. Qiang Yang
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Correspondence to Zhixu Li.

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This article belongs to the Topical Collection: Special Issue on Web and Big Data

Guest Editors: Junjie Yao, Bin Cui, Christian S. Jensen, and Zhe Zhao

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Yang, Q., Li, Z., Liu, A. et al. A novel hybrid publication recommendation system using compound information. World Wide Web 22, 2499–2517 (2019). https://doi.org/10.1007/s11280-019-00687-9

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