Basic Information
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Name：Ren, Gang |
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Gender：Male |
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Discipline：Information Management & Information Systems |
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Professional Title：Instructor |
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E-mail：mregan@hfut.edu.cn |
Personal Introduction
Dr. REN Gang, male, native of Yuncheng City, Shanxi Province, graduated with a doctor's degree in management from the School of Management, Pusan National University, South Korea, in August 2018, and once worked at Kookmin University in Korea after graduation. He has been working as a lecturer in the School of Management of Hefei University of Technology since March 2021. In recent years, he has been mainly engaged in research on business big data analysis, enterprise credit rating, topic analysis and its effectiveness based on users' online reviews. His research methods include data mining, deep learning, laboratory experimental design, econometric analysis, and mixed methods. Currently, he focuses on multimodal data fusion research for graphical interaction and consumer behavior research. His research results have been published in Information Processing & Management, Electronic Commerce Research, Asia Pacific Journal of Information Systems, and many other journals. He is also a peer reviewer for IP&M, TRE, IMDS, ECRA, IT&P, AMCIS, PACIS and other international journals and conferences.
Work Experience
Assistant Professor of Kookmin University, South Korea Mar 2019 - Jan 2021
Education Background
Doctor Pusan National University 2018
Master Pusan National University 2014
Bachelor Jishou University 2011
Awards Received
Intellectual Contribution
Journal Articles
1.DMFN: A disentangled multi-level fusion network for review helpfulness prediction,EXPERT SYSTEMS WITH APPLICATIONS,2023-10-15
2.SUDF-RS: A new foreign exchange rate prediction method considering the complementarity of supervised and unsupervised deep representation features,EXPERT SYSTEMS WITH APPLICATIONS,2023-03-15
Project
1.Research on the usefulness impact mechanism and configuration analysis of online comments based on image-text interaction,2023-01-01,2025-12-31
2.Analysis of Usefulness of Online Reviews Based on Multimodal Attention Mechanism,2021-07-09,2023-07-09
3.Research on Subdivision of Review Usefulness Based on Visual Content,2021-05-01,2023-04-30
Awards
1.National First-class Course,National level,2023-05-30
Courses Taught
