A Submodular Approach for Reference Recommendation

Kieu, TB; Pham, SB; Phan, XH; Piccardi, M

A Submodular Approach for Reference Recommendation

Kieu, TB Pham, SB Phan, XH Piccardi, M

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Publisher:: Springer Singapore
Publication Type:: Conference Proceeding
Citation:: Communications in Computer and Information Science, 2020, 1215 CCIS, pp. 3-14
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Kieu, TB
dc.contributor.author	Pham, SB
dc.contributor.author	Phan, XH
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604
dc.date.accessioned	2021-03-29T06:56:54Z
dc.date.available	2021-03-29T06:56:54Z
dc.date.issued	2020-01-01
dc.identifier.citation	Communications in Computer and Information Science, 2020, 1215 CCIS, pp. 3-14
dc.identifier.isbn	9789811561672
dc.identifier.issn	1865-0929
dc.identifier.issn	1865-0937
dc.identifier.uri	http://hdl.handle.net/10453/147617
dc.description.abstract	© 2020, Springer Nature Singapore Pte Ltd. Choosing appropriate references for a given topic is an important, yet challenging task. The pool of potential candidates is typically very large, in the order of tens of thousands, and growing by the day. For this reason, this paper proposes an approach for automatically providing a reference list for a given manuscript. The approach is based on an original submodular inference function which balances relevance, coverage and diversity in the reference list. Experiments are carried out using an ACL corpus as a source for the references and evaluated by MAP, MRR and precision-recall. The results show the remarkable comparative performance of the proposed approach.
dc.language	en
dc.publisher	Springer Singapore
dc.relation.ispartof	Communications in Computer and Information Science
dc.relation.isbasedon	10.1007/978-981-15-6168-9_1
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in Communications in Computer and Information Science. The final authenticated version is available online at: https://dx.doi.org/10.1007/978-981-15-6168-9_1.	en_US
dc.title	A Submodular Approach for Reference Recommendation
dc.type	Conference Proceeding
utslib.citation.volume	1215 CCIS
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/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2021-03-29T06:56:53Z
pubs.publication-status	Published
pubs.volume	1215 CCIS

Abstract:

© 2020, Springer Nature Singapore Pte Ltd. Choosing appropriate references for a given topic is an important, yet challenging task. The pool of potential candidates is typically very large, in the order of tens of thousands, and growing by the day. For this reason, this paper proposes an approach for automatically providing a reference list for a given manuscript. The approach is based on an original submodular inference function which balances relevance, coverage and diversity in the reference list. Experiments are carried out using an ACL corpus as a source for the references and evaluated by MAP, MRR and precision-recall. The results show the remarkable comparative performance of the proposed approach.

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http://hdl.handle.net/10453/147617