Efficient Resume-Based Re-Education for Career Recommendation in Rapidly Evolving Job Markets

The impact of the COVID-19 pandemic and the introduction of artificial intelligence-based tools created significant job losses across various sectors in all countries around the world. A large portion of these job losses is permanent. Furthermore, the hidden unemployment numbers are higher than currently reported and the impact of Generative Pretrained Transformer (GPT) based tools will further increase the unemployed population in the coming years. Most businesses are likely to experience significant disruptions to their business-as-usual operations and will face business underperformance for long periods. To ensure business continuity and a smooth recovery process following severe disruptions, it is crucial to establish a recovery strategy. To provide enough workforce for the recovery strategy of various businesses, a large-scale rapid re-education of the workforce is required. Intelligent and virtual workplaces will replace traditional offices in various sectors in the upcoming years and many low-skilled jobs are in danger of being permanently lost. In this paper, an artificial intelligence-based framework for rapid work-skill re-education for evolving markets named Career-gAIde is presented. The proposed framework uses automatic analysis of the job resume of the workers for recommendations of a suitable new job with a higher salary and the best rapid re-education path toward that job. Custom build deep neural networks based on CNN-Random along with customized natural language processing tools are designed for large-scale automatic recommendation of a personalized education and career path to each job seeker. The proposed work is focused on software engineering job search and resume upgrades. There is also a book recommendation module for obtaining the knowledge of job seekers. Precision criteria were used to evaluate the job offer recommendations and the proposed framework achieves 67% in this measure. The Recall criteria were used to assess the required skills, with results of 84% and 79%, respectively. The experimental results show that the proposed framework can provide a solution for rapid work-skill re-adjustment for large-scale workforces.