Adaptive and fair job recommendation system using skill evolution and bias mitigation techniques

This paper presents an adaptive job recommendation system that models dynamic skill evolution and integrates fairness-aware learning to mitigate bias. The proposed hybrid framework integrates content-based and collaborative filtering with deep learning to provide personalized, context-aware recommendations. Recurrent neural networks (RNNs) capture temporal skill progression, while BERT-based analysis identifies emerging industry trends. An adversarial debiasing mechanism reduces algorithmic bias by 15%, ensuring equitable outcomes across demographic groups. Achieving 92% accuracy and a 0.92 F1-score, the system demonstrates strong performance in fair and adaptive job matching. A continuous feedback loop further refines recommendations as user interactions evolve. This study advances the ethical use of AI in recruitment by offering a dynamic and fair approach that aligns with both candidate growth and employer needs.