Zhang Wen

Personal profile

Name:Zhang Wen

Gender:male

Degrees:Ph.D

Title:Professor

E-mail:zhangwen@bjut.edu.cn

Current Professional Societies

1. Member of Chinese Society for Systems Sciences;

2. Member of IEEE, ACM, CCF;

3. PC Member of PRICAI and SMC.

Research Areas

1. E-commerce, Recommendation Systems, Web data mining, text data mining, Big data analytics;

2. Mining software repository, software defect prediction, software defect location;

Honors

1. Outstanding Youth Talent of Beijing.

Publications

1.W. Zhang, X. Li, J. Li, Y. Yang.: A Two-stage Rating Prediction Approach Based on Matrix Clustering on Implicit Information. IEEE Transactions on Computational Social Systems, 7(2):517-535, 2020.

2. W. Zhang, Y. Du, T. Yoshida, Y. Yang.: DeepRec: A Deep Neural Network Approach to Recommendation with Item Embedding and Weighted Loss Function. Information Sciences, 470:121-140, 2019.

3. W. Zhang, Y, Du, Y, Yang, T. Yoshida.: DeRec: A Data-driven Approach to Accurate Recommendation with Deep Learning and Weighted Loss Function. Electronic Commerce Research and Applications. 31:12-23. 2018.

4 .W. Zhang, Y. Du, T. Yoshida, Q. Wang: DRI-RCNN: An Approach to Deceptive Review Identification using Recurrent Convolutional Neural Network. Information Processing & Management, 54(4): 576-592, 2018.

5.W. Zhang, L. Yu, T. Yoshida, Q. Wang: Feature Weighted Confidence to Incorporate Prior Knowledge with Support Vector Machines for Classification. Knowledge and Information Systems, 58:371-379, 2019.

Personal statement

Zhang Wen, professor and doctoral supervisor, received the Ph.D. degree in knowledge science from the Japan Advanced Institute of Science and Technology, in 2009. He is currently a Full Professor with the School of Economics and Management, Beijing University of Technology, Beijing, China. He has presided two National Natural Science Foundation of China projects and one Natural Science Fund of Beijing project. He is the author of more than 50 publications, including Information Sciences, Information Processing and Management, and Knowledge and Information Systems. His recent research interests include big data analytics, recommendation systems, information systems, data mining, and social network analysis.