A proactive decision support system for reviewer recommendation in academia

被引:19
作者
Pradhan, Tribikram [1 ,2 ]
Sahoo, Suchit [3 ]
Singh, Utkarsh [3 ]
Pal, Sukomal [1 ]
机构
[1] Indian Inst Technol BHU, Dept Comp Sci & Engn, Varanasi, Uttar Pradesh, India
[2] MAHE, Manipal Inst Technol, Dept Informat & Commun Technol, Manipal, Karnataka, India
[3] Indian Inst Technol BHU, Dept Elect Engn, Varanasi, Uttar Pradesh, India
关键词
Reviewer recommendation; Topic modeling; Clustering; Citation analysis; Random walk with restart (RWR); INFORMATION; INDEX;
D O I
10.1016/j.eswa.2020.114331
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Peer review is an essential part of scientific communications to ensure the quality of publications and a healthy scientific evaluation process. Assigning appropriate reviewers poses a great challenge for program chairs and journal editors for many reasons, including relevance, fair judgment, no conflict of interest, and qualified reviewers in terms of scientific impact. With a steady increase in the number of research domains, scholarly venues, researchers, and papers in academia, manually selecting and accessing adequate reviewers is becoming a tedious and time-consuming task. Traditional approaches for reviewer selection mainly focus on the matching of research relevance by keywords or disciplines. However, in real-world systems, various factors are often needed to be considered. Therefore, we propose a multilayered approach integrating Topic Network, Citation Network, and Reviewer Network into a reviewer Recommender System (TCRRec). We explore various aspects, including relevance between reviewer candidates and submission, authority, expertise, diversity, and conflict of interest and integrate them into the proposed framework TCRRec. The paper also addresses cold start issues for researchers having unique areas of interest or for isolated researchers. Experiments based on the NIPS and AMiner dataset demonstrate that the proposed TCRRec outperforms state-of-the-art recommendation techniques in terms of standard metrics of precision@k, MRR, nDCG@k, authority, expertise, diversity, and coverage.
引用
收藏
页数:20
相关论文
共 50 条
[1]   Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions [J].
Adomavicius, G ;
Tuzhilin, A .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2005, 17 (06) :734-749
[2]  
Anjum O, 2019, 2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019), P518
[3]   Artificial neural networks: fundamentals, computing, design, and application [J].
Basheer, IA ;
Hajmeer, M .
JOURNAL OF MICROBIOLOGICAL METHODS, 2000, 43 (01) :3-31
[4]   Latent Dirichlet allocation [J].
Blei, DM ;
Ng, AY ;
Jordan, MI .
JOURNAL OF MACHINE LEARNING RESEARCH, 2003, 3 (4-5) :993-1022
[5]   Fast unfolding of communities in large networks [J].
Blondel, Vincent D. ;
Guillaume, Jean-Loup ;
Lambiotte, Renaud ;
Lefebvre, Etienne .
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2008,
[6]   Recommender systems survey [J].
Bobadilla, J. ;
Ortega, F. ;
Hernando, A. ;
Gutierrez, A. .
KNOWLEDGE-BASED SYSTEMS, 2013, 46 :109-132
[7]  
Bradley K., 2001, P 12 IR C ART INT CO, V85, P141
[8]  
Buckley C., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, P25, DOI 10.1145/1008992.1009000
[9]   Collaborator recommendation in interdisciplinary computer science using degrees of collaborative forces, temporal evolution of research interest, and comparative seniority status [J].
Chaiwanarom, Paweena ;
Lursinsap, Chidchanok .
KNOWLEDGE-BASED SYSTEMS, 2015, 75 :161-172
[10]  
Charlin Laurent, 2013, TORONTO PAPER MATCHI