Individual Expert Selection and Ranking of Scientific Articles Using Document Length
| Main Authors: | Saputra, Fadly Akbar; Department of Computer Science, Faculty of Mathematics and Natural Science, Bogor Agricultural University, Kampus IPB Darmaga, Bogor 16680,, Djatna, Taufik; Department of Agroindustrial Technology, Faculty of Agricultural Technology, Bogor Agricultural University, Kampus IPB Darmaga, Bogor 16680,, Handoko, Laksana Tri; Indonesian Institute of Science, Sasana Widya Sarwono (SWS) Jend. Gatot Subroto Street 10, South Jakarta |
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| Format: | Article info Document eJournal |
| Bahasa: | eng |
| Terbitan: |
ITB Journal Publisher, LPPM ITB
, 2019
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| Subjects: | |
| Online Access: |
http://journals.itb.ac.id/index.php/jictra/article/view/9181 http://journals.itb.ac.id/index.php/jictra/article/view/9181/4174 |
Daftar Isi:
- 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%).