International Journal of Science, Engineering and TechnologyAuthors: Assistant Professor Shanmuga Priya K, Manikandan S, Karan Singh D, Bharanitharan S, Manikandan S
Abstract: To leverage towards better hiring and recruiting methods the traditional review of resumes is often carried out via simple keyword matching. It records only superficial abilities and credentials. Although candidates are shown the basic skills and qualifications, it misses the deeper competencies such as solving problems, personal development and contributions to projects. This paper proposes an artificially intelligent approach which combines machine learning (ML) for assessing abilities and potential, natural language processing (NLP) for reviewing resumes and job descriptions, and graph-based career mapping to visualize career progression. Compared with traditional resume scoring models, this proposed methodology presents more informed candidate evaluation on all factors including context, experience and growth potential. Professional network analysis, accuracy and quality of input data, as well as candidate skill alignment is one of the important aspects of the proposed model. The graph-based method presents some career paths and the practical contributions to the study via mapping abilities over time. By using our proposed technology approach, hiring decisions can be improved and position matching can be performed optimally. Moreover, context-aware analysis can provide an accurate evaluation of candidate potential. In the field of HR technology, this innovative method has a new standard in fair and intelligent talent evaluation
