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Exploiting Publication Contents and Collaboration Networks for Collaborator Recommendation
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Scholarly documents can be retrieved by crawling and extracting using structural and content-based features.
“Scholarly documents can be retrieved by crawling and extracting using structural and content-based features [19].”
From Scholarly knowledge graphs through structuring scholarly communication: a review (2023) · cites this paper as [19]
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Research works printed but not linked: 23
- S Lee, 2005, The impact of research collaboration on scientific productivity, Soc Stud Sci, 35, 673, 10.1177/0306312705052359 · DOI
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- J Li, 2014, Acrec: a co-authorship based random walk model for academic collaboration recommendation, Proc. WWW, 1209
- F Xia, 2014, Mvcwalker: Random walk based most valuable collaborators recommendation exploiting academic factors, IEEE Trans Emerg Top Comput, 2, 364, 10.1109/TETC.2014.2356505 · DOI
- SD Gollapalli, 2012, Similar researcher search in academic environments, Proc. ACM/IEEE JCDL, 167
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- RN Lichtenwalter, 2010, New perspectives and methods in link prediction, Proc. SIGKDD, 243
- DH Lee, 2011, Recommending collaborators using social features and mesh terms, Proceedings of the American Society for Information Science and Technology, 48, 1
- S Cohen, 2013, Recommending collaborators using keywords, Proc. WWW, 959
- F Xia, 2014, Socially aware conference participant recommendation with personality traits, IEEE Systems Journal
- Chaiwanarom P, Lursinsap C (2014) Collaborator recommendation in interdisciplinary computer science using degrees of collaborative forces, temporal evolution of research interest, and comparative seniority status. Knowledge-Based Systems. · DOI
- J Tang, 2012, Cross-domain collaboration recommendation, Proc. SIGKDD, 1285
- HH Chen, 2011, Collabseer: a search engine for collaboration discovery, Proc. ACM/IEEE JCDL, 231
- C Li, 2014, The author-topic-community model for author interest profiling and community discovery, Knowledge and Information Systems, 1
- Goldberg Y, Levy O (2014) word2vec explained: deriving mikolov et al.’s negative-sampling word-embedding method. arXiv preprint arXiv:14023722.
- DM Blei, 2003, Latent dirichlet allocation, Journal of Machine Learning Research, 3, 993
- Hofmann T (1999) Probabilistic latent semantic indexing. In: Proc. 22nd annual international ACM SIGIR conference on Research and development in information retrieval. pp. 50–57. · DOI
- Cao Z, Li S, Liu Y, Li W, Ji H. (2015) A Novel Neural Topic Model and Its Supervised Extension. Twenty-Ninth AAAI Conference on Artificial Intelligence. · DOI
- M Ley, 2009, Dblp: some lessons learned, Proceedings VLDB Endowment, 2, 1493, 10.14778/1687553.1687577 · DOI
Cited by
Sorted by citations
Scholarly knowledge graphs through structuring scholarly communication: a review
Verma, S., Bhatia, R., Harit, S., & Batish, S. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00806-6 · Cited by 78
Metadata
- Authors
- Kong, X., Jiang, H., Yang, Z., Xu, Z., Xia, F., & Tolba, A.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-02-05
- Volume
- 11
- Issue
- 2
- Pages
- e0148492
- ISSN
- 1932-6203
- PMC
- PMC4743965
- Cited by
- 67
- References
- 0
- Language
- en
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