Towards expert preference on academic article recommendation using bibliometric networks

Yu Zhang, Min Wang, Morteza Saberi, Elizabeth Chang

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationTrends and Applications in Knowledge Discovery and Data Mining - PAKDD 2020 Workshops, DSFN, GII, BDM, LDRC and LBD, Revised Selected Papers
EditorsWei Lu, Kenny Q. Zhu
Place of PublicationSwitzerland
Number of pages9
ISBN (Electronic) 9783030604707
ISBN (Print)9783030604691
Publication statusPublished - 2020
Externally publishedYes
Event24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 - Singapore, Singapore
Duration: 11 May 202014 May 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12237 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020
Internet address


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