A document vectorization approach to Resume Ranking System(RRS)
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Technology transformed the way how job seekers apply for a job and recruiter’s hunting for a precise pick . Now, paper version of resume already become an outdated version of job application method. Electronic resume replaces the old method thanks to its easier access to technology. When it comes to a particular job requirement, screening a rele vant resume among thousands is an exhaustive and time consuming recruitment process because the respective HR of an organization must have a proof read the entire resume set to select the right person in the right position, a key decision for any organization. Extracting the semantic meaning from resume is otherwise a daunting task. By making the selection process fast and accurate, organizations could save huge efforts and money. Using state-of-art-technology could be a way out. In the field of NLP, there are a range of tools to classify documents. Document vectorization technique is a huge popular one among tech-communities. Documents like resumes could be categorized and ranked by applying such techniques and tools. Therefore choosing a most suitable vectorization al gorithm is pivotal. It is aimed to build a custom trained model specialized in vocabulary of resume based on frequency based word2vec model such as TF-IDF. However, to compare between job descriptions and resumes, Cosine-Similarity is consid ered to be the primary algorithm to find matching resumes whereas k-nearest neighbor algorithm has been used to group the desired documents. But the limitation comes with using fixed vocabulary size. TOPSIS is the most popular among Multi Criteria Decision Making algorithms. Along with vector similarity score, Other parameters like years of experience, university rankings could be normalized to consider for final ranking score.