Revolutionizing Talent Acquisition in Human Resources with Advanced Deep Learning-Based Resume Screening and Candidate Matching Algorithms

In general, this paper outlines a research agenda to transform the approach to talent management in HR using deep learning strategies for enhanced resume analysis and candidate matching. The above work proposed and developed a system that exploits the most advanced tools of natural language processing, and recent advances in neural networks to correctly parse resumes and the candidates to jobs descriptions. The system deals with the issues of unstructured resume data, the efficient extraction of the required data, and assessment of the candidate on his/her fitness in the given company in terms of his/her technical competencies and compatibility with the company's work culture. Substantial real life data analysis presented in the paper proves high efficiency and equity of the applications. This research also incorporates the ethical questions as well as the ways of dealing with data privacy regulations and the biases in the historical data by applying the explainable AI methods. A questionnaire to the users of the system, which are the HR professionals further verifies the real-world application and usability of the system based on concepts such as ease of use, precision and time economy. In conclusion, the study adds knowledge to the process of establishing ethical and optimal AI implementations in the field of HRM and results in the improvement of talent recruitment strategies and practices promoting fair hiring decisions.