Abstract

The recruitment process is a critical phase for companies looking to hire competent and well-suited employees. However, traditional methods for evaluating applicants can be time-consuming, subjective and prone to bias. This study presents a decision support system that uses machine learning techniques to support the evaluation of job applicants based on qualifications and personality traits. The system processes input data from applicants' CVs and interview videos to extract relevant characteristics such as educational background, skills, certifications, work experience and the Big Five personality traits (openness, conscientiousness, extraversion, agreeableness and neuroticism). Resume data is analyzed using Natural Language Processing and keyword matching to assess qualifications, while video features are processed using audio-visual analysis and ML models to predict personality traits. The extracted data is then matched against employer-defined criteria and applicants are ranked according to their overall suitability. The proposed system aims to streamline the recruitment process, reduce human bias and improve the objectivity and efficiency of applicant assessment. The results show the potential of integrating ML into recruitment workflows for more informed and data-driven decision making.

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