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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Alqudah, Nour
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Computer Science & IT Engineering

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Improving job matching with deep learning-based hyper-personalization Abuein, Qusai Q.; Shatnawi, Mohammed Q.; Alqudah, Nour
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1711-1722

Abstract

This study introduces a novel approach to streamline the recruitment process, benefiting both employers and job seekers. It leverages real-time personality-based classification to match candidates with the most suitable roles in a scalable and precise manner. This is achieved through machine learning-driven hyper-personalization, employing deep learning models to create a predictive language model. The study encompasses two key tasks: binary classification, distinguishing sentences containing soft skills (1) from those that do not (0), and multi-class classification, categorizing positive sentences into five classes based on Big Five personality traits. The research involved a series of experiments. Initially, multiple machine learning algorithms were employed to establish baseline models. Subsequently, the study investigated the impact of deep learning versus these baseline models. The results demonstrated an accuracy of 0.79% and 0.68% for binary classification tasks, and 0.79% and 0.60% for multi-class classification tasks, using Support Vector Machines in the machine learning task, and Bidirectional Long Short-Term Memory in the deep learning task, respectively. This approach showcases promise in revolutionizing the job matching process, offering a more efficient and accurate means of connecting individuals with their ideal employment opportunities based on their unique soft skills and personality traits.
AI-driven hyper-personalization and transfer learning for precision recruitment Alqudah, Nour; Abuein, Qusai Q.; Shatnawi, Mohammed Q.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4271-4278

Abstract

The research study demonstrates how artificial intelligence (AI)-powered models can transform the hiring process by maximizing the match between candidates and jobs, leading to better hiring options and increased worker productivity. Our research develops highly personalized AI-powered recruitment applications. By using hyper-personalization to tailor job recommendations based on job compatibility and big five personality traits, this study leverages AI to improve job matching. Unlike traditional recruitment models that depend only on complex skill matching, hyper-personalization combines soft skills and personality dimensions to achieve a more precise candidate-job alignment. Transformer-based models, including bidirectional encoder representations from transformers (BERT), RoBERTa, and cross-lingual language model (XLM)-RoBERTa, have shown exceptional performance in natural language processing (NLP) and classification tasks; thus, we apply them. Transfer learning helps us to fine-tune these models to improve the accuracy of personality classification. Compared to conventional models, experimental data achieves up to 80% accuracy in binary classification and 72% in multi-class classification. By demonstrating job-candidate compatibility, this study emphasizes the potential of AI-driven models to transform recruitment, leading to better hiring decisions and workforce productivity. Our outcomes play a crucial role in advancing hyper-personalized AI applications in talent.
Co-Authors Abuein, Qusai Q. Shatnawi, Mohammed Q.