Speaker: Shachar Wild
Title: Cost-Oriented Candidate Screening Using Machine Learning Algorithms
Choosing the right candidates for any kind of position, whether it is for academic studies or for a professional job, is not an easy task, since each candidate has multiple traits, which may impact her or his success probability in a different way. Furthermore, admitting inappropriate candidates and leaving out the right ones may incur significant costs to the screening organization. Therefore, such a candidate selection process requires a lot of time and resources. In this paper, we treat this task as a cost optimization problem and use machine learning methods to predict the most cost-effective number of candidates to admit, given a ranked list of all candidates and a cost function. This is a general problem, which applies to various domains, such as: job candidate screening, student admission, document retrieval, and diagnostic testing. We conduct comprehensive experiments on two real-world case studies that demonstrate the effectiveness of the proposed method in finding the optimal number of admitted candidates.
Shachar Wild, M.Sc student in Software and Information Systems Engineering at BGU, specializing in Computational Learning & Big Data. B.Sc in Software and Information Systems Engineering was acquired at BGU. Currently working as a Data Scientist at SAP.