TY - GEN
T1 - Cost-Oriented Candidate Screening Using Machine Learning Algorithms
AU - Wild, Shachar
AU - Last, Mark
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022/11/24
Y1 - 2022/11/24
N2 - 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.
AB - 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.
KW - Candidate screening
KW - Candidate list truncation
KW - Prediction models
KW - Constrained optimization
KW - Asymmetric error costs
UR - http://www.scopus.com/inward/record.url?scp=85144199425&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-8234-7_57
DO - 10.1007/978-981-19-8234-7_57
M3 - Conference contribution
SN - 9789811982330
T3 - Communications in Computer and Information Science
SP - 737
EP - 750
BT - Recent Challenges in Intelligent Information and Database Systems - 14th Asian Conference, ACIIDS 2022, Proceedings
A2 - Szczerbicki, Edward
A2 - Wojtkiewicz, Krystian
A2 - Nguyen, Sinh Van
A2 - Pietranik, Marcin
A2 - Krótkiewicz, Marek
PB - Springer Science and Business Media Deutschland GmbH
CY - Singapore
T2 - 14th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2022
Y2 - 28 November 2022 through 30 November 2022
ER -