Automating Curriculum Vitae Recommendation Processes Through Machine Learning

Most businesses now use Internet-based recruiting portals as their main hiring method. Such platforms save time and expenses associated with hiring new employees, but they have problems with outdated information retrieval strategies like Boolean search approaches. With an emphasis on two properties, we describe a CV recommender system in this study. The first characteristic is the capacity to automatically process candidates’ CV papers and classify them into positions. The capacity to suggest abilities to a candidate that are not included in their CV but are probably present is the second quality. Both features are based on skill extraction from a textual CV document, both features. For candidate categorization, a precomputed spectral skill clustering is used, and different similarity-based techniques are used for skill suggestion. In this project, we will utilize machine learning techniques such as Ngram and WordCloud to find the talents that are absent from the resume and to offer replacements. The experimental results show the efficiency of the suggested strategies through both automatic experiments and an empirical investigation.