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Individual Expert Selection and Ranking of Scientific Articles Using Document Length

Journal of ICT Research and Applications (JICTRA) · Vol. 13 No. 1 (2019) · pp. 36–49

DOI
10.5614/itbj.ict.res.appl.2019.13.1.3
ISSN
2338-5499, 2337-5787
Published
2019-04-30
Publisher
DRI - ITB
Language
eng

Abstract

Individual expert selection and ranking is a challenging research topic that has received a lot attention in recent years because of its importance related to referencing experts in particular domains and research fund allocation and management. In this work, scientific articles were used as the most common source for ranking expertise in particular domains. Previous studies only considered title and abstract content using language modeling. This study used the whole content of scientific documents obtained from Aminer citation data. The modified weighted language model (MWLM) is proposed that combines document length and number of citations as prior document probability to improve precision. Also, the author's dominance in a single document is computed using the Learning-to-Rank (L2R) method. The evaluation results using p@n, MAP, MRR, r-prec, and bpref showed a precision enhancement. MWLM improved the weighted language model (WLM) by p@n (4%), MAP (22.5%), and bpref (1.7%). MWLM also improved the precision of a model that used author dominance by MAP (4.3%), r-prec (8.2%), and bpref (2.1%).

Keywords

Collaboration network of the authors

7 authors · 13 co-authorship links · 5 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • IPB University
  • Indonesian Institute of Sciences
  • Syarif Hidayatullah State Islamic University Jakarta
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ditdit Nugeraha UtamaSyarif Hidayatullah State Islamic University Jakarta1
Erliza HambaliIPB University1
Marimin .Affiliation unknown1
Dadan KusdianaIPB University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Individual Expert Selection and Ranking of Scientific Articles Using Document Length</dc:title>
	<dc:creator>Saputra, Fadly Akbar</dc:creator>
	<dc:creator>Djatna, Taufik</dc:creator>
	<dc:creator>Handoko, Laksana Tri</dc:creator>
	<dc:subject xml:lang="en-US">document length</dc:subject>
	<dc:subject xml:lang="en-US">individual expert</dc:subject>
	<dc:subject xml:lang="en-US">language model</dc:subject>
	<dc:subject xml:lang="en-US">scientific article</dc:subject>
	<dc:subject xml:lang="en-US">selection and ranking</dc:subject>
	<dc:description xml:lang="en-US">Individual expert selection and ranking is a challenging research topic that has received a lot attention in recent years because of its importance related to referencing experts in particular domains and research fund allocation and management. In this work, scientific articles were used as the most common source for ranking expertise in particular domains. Previous studies only considered title and abstract content using language modeling. This study used the whole content of scientific documents obtained from Aminer citation data. The modified weighted language model (MWLM) is proposed that combines document length and number of citations as prior document probability to improve precision. Also, the author's dominance in a single document is computed using the Learning-to-Rank (L2R) method. The evaluation results using p@n, MAP, MRR, r-prec, and bpref showed a precision enhancement. MWLM improved the weighted language model (WLM) by p@n (4%), MAP (22.5%), and bpref (1.7%). MWLM also improved the precision of a model that used author dominance by MAP (4.3%), r-prec (8.2%), and bpref (2.1%).</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2019-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
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	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/9181</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2019.13.1.3</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 13 No. 1 (2019); 36-49</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2019.13.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/9181/4174</dc:relation>
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