A Pattern-Based Academic Reviewer Recommendation Combining Author-Paper and Diversity Metrics
With the rapid increase of publishable research articles and manuscripts, the pressure to find reviewers often overwhelms the journal editors. This paper incorporates the major entity level metrics found in the heterogeneous publication networks into a pattern mining process in order to recommend ac...
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doaj-5310f943d98c4cacbd380a89044cf4342021-03-29T22:23:37ZengIEEEIEEE Access2169-35362019-01-017164601647510.1109/ACCESS.2019.28946808625579A Pattern-Based Academic Reviewer Recommendation Combining Author-Paper and Diversity MetricsMusa Ibrahim Musa Ishag0https://orcid.org/0000-0002-5315-7314Kwang Ho Park1Jong Yun Lee2Keun Ho Ryu3https://orcid.org/0000-0003-0394-9054College of Electrical and Computer Engineering, Chungbuk National University, Cheongju, South KoreaCollege of Electrical and Computer Engineering, Chungbuk National University, Cheongju, South KoreaCollege of Electrical and Computer Engineering, Chungbuk National University, Cheongju, South KoreaFaculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, VietnamWith the rapid increase of publishable research articles and manuscripts, the pressure to find reviewers often overwhelms the journal editors. This paper incorporates the major entity level metrics found in the heterogeneous publication networks into a pattern mining process in order to recommend academic reviewers and potential research collaborators. In essence, the paper integrates authors' h-index and papers' citation count and proposes a quantification to account for the author diversity into one formula duped impact to measure the real influence of a scientific paper. Thereafter, this paper formulates two kinds of target patterns and mines them harnessing the high-utility itemset mining (HUIM) framework. The first pattern, researcher-general topic patterns (RGP), is a pattern that includes only researchers; whereas, the researcher-specific topic patterns (RSP) is comprised of combinations of researchers and keywords that summarize their niche of expertise. The HUI algorithms of Two Phase, IHUP, UP-Growth, FHM, FHN, HUINIV-Mine, D2HUP, and EFIM were compared on two real-world citation datasets related to Deep Learning and HUIM, in addition to the open source mushroom dataset. The EFIM algorithm showed good performance in terms of run time and memory usage. Consequently, it was then used to mine the patterns within the proposed framework. The discovered patterns of RGP and RSP showed high coverage, proving the efficiency of the proposed framework.https://ieeexplore.ieee.org/document/8625579/High utility itemset miningrecommender systemexpert findingscholarly big datareviewer assignment |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Musa Ibrahim Musa Ishag Kwang Ho Park Jong Yun Lee Keun Ho Ryu |
spellingShingle |
Musa Ibrahim Musa Ishag Kwang Ho Park Jong Yun Lee Keun Ho Ryu A Pattern-Based Academic Reviewer Recommendation Combining Author-Paper and Diversity Metrics IEEE Access High utility itemset mining recommender system expert finding scholarly big data reviewer assignment |
author_facet |
Musa Ibrahim Musa Ishag Kwang Ho Park Jong Yun Lee Keun Ho Ryu |
author_sort |
Musa Ibrahim Musa Ishag |
title |
A Pattern-Based Academic Reviewer Recommendation Combining Author-Paper and Diversity Metrics |
title_short |
A Pattern-Based Academic Reviewer Recommendation Combining Author-Paper and Diversity Metrics |
title_full |
A Pattern-Based Academic Reviewer Recommendation Combining Author-Paper and Diversity Metrics |
title_fullStr |
A Pattern-Based Academic Reviewer Recommendation Combining Author-Paper and Diversity Metrics |
title_full_unstemmed |
A Pattern-Based Academic Reviewer Recommendation Combining Author-Paper and Diversity Metrics |
title_sort |
pattern-based academic reviewer recommendation combining author-paper and diversity metrics |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
With the rapid increase of publishable research articles and manuscripts, the pressure to find reviewers often overwhelms the journal editors. This paper incorporates the major entity level metrics found in the heterogeneous publication networks into a pattern mining process in order to recommend academic reviewers and potential research collaborators. In essence, the paper integrates authors' h-index and papers' citation count and proposes a quantification to account for the author diversity into one formula duped impact to measure the real influence of a scientific paper. Thereafter, this paper formulates two kinds of target patterns and mines them harnessing the high-utility itemset mining (HUIM) framework. The first pattern, researcher-general topic patterns (RGP), is a pattern that includes only researchers; whereas, the researcher-specific topic patterns (RSP) is comprised of combinations of researchers and keywords that summarize their niche of expertise. The HUI algorithms of Two Phase, IHUP, UP-Growth, FHM, FHN, HUINIV-Mine, D2HUP, and EFIM were compared on two real-world citation datasets related to Deep Learning and HUIM, in addition to the open source mushroom dataset. The EFIM algorithm showed good performance in terms of run time and memory usage. Consequently, it was then used to mine the patterns within the proposed framework. The discovered patterns of RGP and RSP showed high coverage, proving the efficiency of the proposed framework. |
topic |
High utility itemset mining recommender system expert finding scholarly big data reviewer assignment |
url |
https://ieeexplore.ieee.org/document/8625579/ |
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