Development of a new predictive hiring system with multi-model voting sets and advanced stacking techniques for assessing semantic soft skills
DOI:
https://doi.org/10.56294/dm20261287Keywords:
predictive hiring, soft skills, machine learning, embeddings models, semantic data base, AI in HR, automation of the recruitment process, BERTAbstract
Human resources face a major challenge in extracting and identifying the semantic correspondence of data, in particular the soft skills most recruiters seek from heterogeneous data. The complexity lies in identifying the relationships between textual descriptions in CVs, keywords, descriptions in professional networks, and relevant soft skills such as communication, persuasion skills, negotiation, relationship building, empathy, teamwork, conflict resolution, emotional intelligence, time management, work ethics, after analysis and research, we chose these soft skills as input data because they encompass all the soft skills that a recruiter might look for in a candidate for any position. The present study introduces a predictive hiring system that assesses candidate performance based on soft skills extracted from three main sources, namely resumes, professional social network profiles, and psychometric assessments. A dataset of over one million candidate records was processed. Data analysis relied on state-of-the-art NLP techniques, including word embeddings and contextual language models, in order to build a semantic database linking keywords, phrases, and descriptions to targeted soft skills. Machine and deep learning models were applied, followed by an ensemble approach integrating KNN, Decision Tree, and Random Forest. To overcome prediction accuracy and overfitting limitations, a meta-model XGBoost was developed, achieving superior results with an accuracy of 98%. The results demonstrate that the proposed meta-model outperforms baseline approaches, delivering high predictive accuracy and robust generalization. These findings highlight the potential of combining semantic analysis with advanced machine learning to support more reliable and scalable predictive recruitment systems.
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