EDBT 2026 Demo / reviewers in the wild / expert
Feng Wang 0048
dblp:90/4225-48
· DBLP profile ↗
8ranked-venue papers in the field
4as first author
3since 2021 · last 2022
0000-0003-2109-9719ORCID · conflict
Domains — venue-derived; a paper can count in several
Knowledge Engineering, Semantic Web & Information Systems · 6 (4 first)Database Systems & Data Management · 1Information Retrieval & Web Search · 1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | A reinforcement learning level-based particle swarm optimization algorithm for large-scale optimization
Feng Wang 0048, Xujie Wang, Shilei Sun |
Inf. Sci. | 1 |
| 2021 | Good for use, but better for choice: A relative model of competing social networking services
Xiao-Liang Shen 0001, Yangjun Li, Yongqiang Sun, Feng Wang 0048 |
Inf. Manag. | 4 |
| 2021 | A new prediction strategy for dynamic multi-objective optimization using Gaussian Mixture Model
Feng Wang 0048, Fanshu Liao, Hui Wang 0002 |
Inf. Sci. | 1 |
| 2020 | A hybrid convolution network for serial number recognition on banknotes
Feng Wang 0048, Huiqing Zhu, Wei Li 0078, Kangshun Li |
Inf. Sci. | 1 |
| 2019 | Understanding the role of technology attractiveness in promoting social commerce engagement: Moderating effect of personal interest
Xiao-Liang Shen 0001, Yangjun Li, Yongqiang Sun, Zhen-Jiao Chen, Feng Wang 0048 |
Inf. Manag. | 5 |
| 2019 | Knowledge withholding in online knowledge spaces: Social deviance behavior and secondary control perspectiveabstractKnowledge withholding, which is defined as the likelihood that an individual devotes less than full effort to knowledge contribution, can be regarded as an emerging social deviance behavior for knowledge practice in online knowledge spaces. However, prior studies placed a great emphasis on proactive knowledge behaviors, such as knowledge sharing and contribution, but failed to consider the uniqueness of knowledge withholding. To capture the social‐deviant nature of knowledge withholding and to better understand how people deal with counterproductive knowledge behaviors, this study develops a research model based on the secondary control perspective. Empirical analyses were conducted using the data collected from an online knowledge space. The results indicate that both predictive control and vicarious control exert a positive influence on knowledge withholding. This study also incorporates knowledge‐withholding acceptability as a moderating variable of secondary control strategies. In particular, knowledge‐withholding acceptability enhances the impact of predictive control, whereas it weakens the effect of vicarious control on knowledge withholding. This study concludes with a discussion of the key findings, and the implications for both research and practice. Xiao-Liang Shen 0001, Yangjun Li, Yongqiang Sun, Jun Chen 0020, Feng Wang 0048 |
J. Assoc. Inf. Sci. Technol. | 5 |
| 2018 | A hybrid particle swarm optimization algorithm using adaptive learning strategy
Feng Wang 0048, Kangshun Li, Zhiyi Lin 0001, Xiao-Liang Shen 0001 |
Inf. Sci. | 1 |
| 2011 | Improving Stock Market Prediction by Integrating Both Market News and Stock Prices
Xiaodong Li 0007, Feng Wang 0048, Xiaotie Deng, Shanfeng Zhu |
DEXA (2) | 4 |