Towards expert preference on academic article recommendation using bibliometric networks

Zhang, Y; Wang, M; Saberi, M; Chang, E

Towards expert preference on academic article recommendation using bibliometric networks

Zhang, Y Wang, M Saberi, M

Chang, E

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Trends and Applications in Knowledge Discovery and Data Mining, 2020, 12237 LNAI, pp. 11-19
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Zhang, Y
dc.contributor.author	Wang, M
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Chang, E
dc.date	2020-05-11
dc.date.accessioned	2021-04-19T00:40:21Z
dc.date.available	2021-04-19T00:40:21Z
dc.date.issued	2020-01-01
dc.identifier.citation	Trends and Applications in Knowledge Discovery and Data Mining, 2020, 12237 LNAI, pp. 11-19
dc.identifier.isbn	9783030604691
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/148194
dc.description.abstract	Expert knowledge can be valuable for academic article recommendation, however, hiring domain experts for this purpose is rather expensive as it is extremely demanding for human to deal with a large volume of academic publications. Therefore, developing an article ranking method which can automatically provide recommendations that are close to expert decisions is needed. Many algorithms have been proposed to rank articles but pursuing quality article recommendations that approximate to expert decisions has hardly been considered. In this study, domain expert decisions on recommending quality articles are investigated. Specifically, we hire domain experts to mark articles and a comprehensive correlation analysis is then performed between the ranking results generated by the experts and state-of-the-art automatic ranking algorithms. In addition, we propose a computational model using heterogeneous bibliometric networks to approximate human expert decisions. The model takes into account paper citations, semantic and network-level similarities amongst papers, authorship, venues, publishing time, and the relationships amongst them to approximate human decision-making factors. Results demonstrate that the proposed model is able to effectively achieve human expert-alike decisions on recommending quality articles.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Trends and Applications in Knowledge Discovery and Data Mining
dc.relation.ispartof	Pacific-Asia Conference on Knowledge Discovery and Data Mining
dc.relation.ispartofseries	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-030-60470-7_2
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Towards expert preference on academic article recommendation using bibliometric networks
dc.type	Conference Proceeding
utslib.citation.volume	12237 LNAI
utslib.location.activity	Singapore
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-19T00:40:20Z
pubs.finish-date	2020-05-14
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2020-05-11
pubs.volume	12237 LNAI
dc.location	Switzerland

Abstract:

Expert knowledge can be valuable for academic article recommendation, however, hiring domain experts for this purpose is rather expensive as it is extremely demanding for human to deal with a large volume of academic publications. Therefore, developing an article ranking method which can automatically provide recommendations that are close to expert decisions is needed. Many algorithms have been proposed to rank articles but pursuing quality article recommendations that approximate to expert decisions has hardly been considered. In this study, domain expert decisions on recommending quality articles are investigated. Specifically, we hire domain experts to mark articles and a comprehensive correlation analysis is then performed between the ranking results generated by the experts and state-of-the-art automatic ranking algorithms. In addition, we propose a computational model using heterogeneous bibliometric networks to approximate human expert decisions. The model takes into account paper citations, semantic and network-level similarities amongst papers, authorship, venues, publishing time, and the relationships amongst them to approximate human decision-making factors. Results demonstrate that the proposed model is able to effectively achieve human expert-alike decisions on recommending quality articles.

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http://hdl.handle.net/10453/148194