Dr. Hui Xiong received a PhD in computer science from the University of Minnesota - Twin Cities in 2005. He is a co-editor-in-chief of the Encyclopedia of GIS (Springer, 1st Edition in 2008, 2nd Edition in 2017) as well as an associate editor of IEEE Transactions on Data and Knowledge Engineering (TKDE), IEEE Transactions on Big Data, ACM Transactions on Knowledge Discovery from Data, and ACM Transactions on Management Information Systems. He also served as a program co-chair (2013) and general co-chair (2015) for the IEEE International Conference on Data Mining (ICDM) and a program co-chair (2018) for the Research Track of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), two flagship conferences in the field of data mining. For his outstanding contributions to data mining and mobile computing, Dr. Xiong was elected an ACM Distinguished Scientist in 2014. Right now, Dr. Xiong is on leave from Rutgers University and serving as the chief scientist of Baidu Inc.
Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria ApproachTong Xu, Hengshu Zhu, Chen Zhu, Pan Li, Hui Xiong
To cope with the accelerating pace of technological changes, talents are urged to add and refresh their skills for staying in active and gainful employment.Business Intelligence
A Joint Learning Approach to Intelligent Job Interview AssessmentDazhong Shen, Hengshu Zhu, Chen Zhu, Tong Xu, Chao Ma, Hui Xiong
The job interview is considered as one of the most essential tasks in talent recruitment, which forms a bridge between candidates and employers in fitting the right person for the right job. While substantial efforts have been made on improving the job interview process, it is inevitable to have biased or inconsistent interview assessment due to the subjective nature of the traditional interview process. To this end, in this paper, we propose a novel approach to intelligent job interview assessment by learning the large-scale real-world interview data. Specifically, we develop a latent variable model named Joint Learning Model on Interview Assessment (JLMIA) to jointly model job description, candidate resume and interview assessment. JLMIA can effectively learn the representative perspectives of different job interview processes from the successful job interview records in history.Business Intelligence