2020-01-06 | Qingxin Meng: Intelligent Talent Recruitment Analytics



The presentation will mainly include two parts related to talent recruitment: Job mobility Prediction and Salary Benchmarking. Firstly, the understanding of job mobility can benefit talent  management operations in a number of ways, such as talent recruitment, talent development, and  talent retention. While there is extensive literature showing the predictability of the organizationlevel job mobility patterns, there are no effective solutions for supporting the understanding of job  mobility at an individual level. We propose a hierarchical career-path-aware neural network for learning individual-level job mobility. Specifically, we aim at answering two questions related to individuals in their career paths: 1) who will be the individual’s next employer? 2) how long will the individual work in the new position?

Secondly, as a vital process to the success of the organization, salary benchmarking aims at identifying the right market rate for each job position. Traditional approaches for salary benchmarking heavily rely on the experiences from domain experts and limited market survey data,  which have difficulties in handling the dynamic scenarios with the timely benchmarking requirement.  We develop a Holistic Salary Benchmarking Matrix Factorization (HSBMF) model for predicting the  missing salary information in the salary matrix by considering confounding factors, such as company similarity, job similarity, and spatial-temporal similarity.




Qingxin Meng is currently a Ph.D. candidate in the Department of Management Science and  Information Systems at Rutgers - The State University of New Jersey, USA. She received her B.E.  degree in Mechanical Engineering from the University of Science and Technology of China  (USTC), Hefei, China. Her general areas of research are data mining, people analytics with the  application in intelligent talent management. She focuses on developing efficient and effective datadriven techniques for addressing various challenges in talent management. She has published various  research papers including a paper in the prestige ACM SIGKDD International Conference on  Knowledge Discovery and Data Mining.