A novel hybrid publication recommendation system using compound information

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

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)2499-2517
Number of pages19
JournalWorld Wide Web
Issue number6
StatePublished - May 17 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): FCC/1/1976-19-01
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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