An AI-Based Shortlisting Model for Sustainability of Human Resource Management
Abstract
:1. Introduction
2. Background
2.1. Recruitment Process
2.2. Recruitment Process with AI
2.3. Some AI-Related Research in HRM
2.4. Opportunities in AI Applications for HRM
2.4.1. Chatbots and NLP in Recruitment
2.4.2. Data Mining for AI
3. Methodology
3.1. Minimum Description Length
- The length, in bits, of the description of the theory; and
- The length, in bits, of data when encoded with the help of the theory.
3.2. Support Vector Machine
3.3. Data
3.4. Feature Selection
- BD_10059: Indicates that they have experience with the “Analysis” position.
- BY_1: Language knowledge “English” is important.
- BD_10021: They have past experience as “Software Developer”.
- BSS_26: Has the knowledge for “Analysis”.
- B_CALISMA_DURUMU: If they are working currently or not.
- BD_GECICI_TABLO_EVT__: Indicates that a candidate has an experience in any job.
3.5. Feature Extraction
- Extract the most important information from the original dataset.
- Compress the dataset keeping only this important information.
- Simplify dataset annotation.
3.6. Machine Learning Model and Experiments
4. Conclusions and Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Model | Setting | Value |
---|---|---|
No Feature Selection | Value of complexity factor | 10 |
No Feature Selection | Number of Iteration | 30 |
No Feature Selection | SVM Solver | SVMS_SOLVER_IPM |
No Feature Selection | Convergence Tolerance | 0.0001 |
No Feature Selection | Kernel | SVMS_LINEAR |
No Feature Selection | Number of Features | 82 |
Model | Setting | Value |
---|---|---|
No Feature Selection | Value of complexity factor | 10 |
No Feature Selection | Number of Iteration | 30 |
No Feature Selection | SVM Solver | SVMS_SOLVER_IPM |
No Feature Selection | Convergence Tolerance | 0.0001 |
No Feature Selection | Kernel | SVMS_LINEAR |
No Feature Selection | Number of Features | 6 |
Model | Setting | Value |
---|---|---|
No Feature Selection | SVM Solver | SVMS_SOLVER_IPM |
No Feature Selection | Number of Iteration | 11 |
No Feature Selection | Value of Standard Deviation | 1.7320 |
No Feature Selection | Convergence Tolerance | 0.001 |
No Feature Selection | Kernel | Gaussian |
No Feature Selection | Number of Features | 6 |
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Aydın, E.; Turan, M. An AI-Based Shortlisting Model for Sustainability of Human Resource Management. Sustainability 2023, 15, 2737. https://doi.org/10.3390/su15032737
Aydın E, Turan M. An AI-Based Shortlisting Model for Sustainability of Human Resource Management. Sustainability. 2023; 15(3):2737. https://doi.org/10.3390/su15032737
Chicago/Turabian StyleAydın, Erdinç, and Metin Turan. 2023. "An AI-Based Shortlisting Model for Sustainability of Human Resource Management" Sustainability 15, no. 3: 2737. https://doi.org/10.3390/su15032737