Automated Personality-Based Candidate Shortlisting using Machine Learning and Natural Language Processing

Anusha, K. O. Vedasree; Dhar, Akanksha; Dixit, Shashwat; Saraf, Amay; Lonial, Ishaan; N, Anusha

doi:10.1109/icaiss58487.2023.10250633

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…However, the recruitment process for these critical positions often faces challenges, especially in objectively assessing the various attributes of candidates that may affect future performance [3].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Recruitment of Prospective Financial Managers Through a Decision Support System with the Multi Attribute Utility Theory Method

Firmansyah,

Sahara,

Yeyi Gusla Nengsih

et al. 2024

E-Komtek

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This study evaluates the application of Multi Attribute Utility Theory (MAUT)-based decision support systems in the recruitment process of financial managers in technology companies "TechInnovate". This study aims to identify the extent to which the use of MAUT can increase objectivity and effectiveness in candidate selection. The quantitative method was used by collecting data from 50 candidates who volunteered for this position. The assessment criteria that have been determined include Education and Qualifications, Work Experience, Analytical and Financial Skills, Communication Skills, Leadership and Team Management, Ethics and Integrity, Technology Capabilities, Adaptability and Innovation, Decision Making Ability. Data is collected through scores assigned to each criterion for each candidate, which are then aggregated taking into account the relative weights of each criterion. Statistical analysis is applied to evaluate the distribution of scores and the effectiveness of the system in selecting the most suitable candidates. Results from this study show that the application of MAUT is significant in improving objectivity in recruitment decision making, with high scores strongly correlated with expected job performance in in-depth interviews and follow-up evaluations. This study proves that integrating a systematic approach in recruitment not only strengthens the selection process but also promotes data-driven and objective decisions. The implications of this research are relevant for companies operating in dynamic and technology-centric environments, suggesting the integration of MAUT-based decision support systems as an effective strategy in the recruitment process.

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Section: Introductionmentioning

confidence: 99%

Optimization of Recruitment of Prospective Financial Managers Through a Decision Support System with the Multi Attribute Utility Theory Method

Firmansyah,

Sahara,

Yeyi Gusla Nengsih

et al. 2024

E-Komtek

View full text Add to dashboard Cite

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An example of the application of artificial intelligence models in human resources processes

Sürücü,

Küçük,

Aydın

2024

KOCATEPEİİBFD

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Creating job postings and selecting suitable candidates among these job postings is a challenging process. This process increases the workload of human resources and causes the process to proceed slowly. It is of great importance for human resources departments to utilize information processing technologies to create job postings effectively and to evaluate the CVs of applicants to these postings. This study introduces and analyzes two different technologies that can help human resources. In the process of preparing job advertisements in the field of IT, in the first stage, the word cloud method is used to decide which keywords should be emphasized in the advertisement texts. In the second stage, the resumes of the applicants are analyzed using three different deep learning models such as CNN (Convolutional Neural Network), GRU (Gated Recurrent Unit), and LSTM (Long Short-Term Memory) for classification purposes. While the performance of these models is evaluated using metrics such as accuracy, MCC, F_1 score, and MSE, the decision-making processes of the models with explainable artificial intelligence are also analyzed. In this context, the GRU model, which achieved an accuracy of 99%, provided the most superior result in this study and the literature. This research shows that deep learning models provide high accuracy rates and efficiency in human resources resume classification and candidate matching processes. It also explains that using the word cloud method, the most appropriate keywords can be identified, and advertisements can be created.

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Automated Personality-Based Candidate Shortlisting using Machine Learning and Natural Language Processing

Cited by 2 publications

References 9 publications

Optimization of Recruitment of Prospective Financial Managers Through a Decision Support System with the Multi Attribute Utility Theory Method

Optimization of Recruitment of Prospective Financial Managers Through a Decision Support System with the Multi Attribute Utility Theory Method

An example of the application of artificial intelligence models in human resources processes

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