Resume Classification & Filtering System

Abstract

"In talent acquisition, efficiently and accurately identifying the most suitable candidates from a pool of resumes is a significant challenge for recruiters and HR professionals. The manual process of reviewing each resume in detail is time-consuming and often causes delays in the hiring process. To solve this problem, we present a resume classification and filtering system that aims to optimize the recruitment process in terms of speed, efficiency and accuracy. By automating this process, recruiters can quickly sift through vast amounts of resumes and filter out the most valuable candidates. Our solution consists of two steps: classification and filtering. In the classification step, we use a linear support vector machine (SVM) model to classify resumes into distinct groups based on their content and attributes. This enables recruiters to efficiently organize and analyze resumes, saving significant time and effort. Moving on to the filtering step, we use the Cosine Similarity algorithm to measure the similarity between the content of each resume and the corresponding job description. By providing matching scores, we can easily identify resumes that closely align with the job requirements. This two-step approach streamlines the entire recruitment process, making it more efficient and accurate. In evaluating the performance of our system, we used various data science metrics to assess its effectiveness. To measure the accuracy of the classification step, we compared the categories assigned by the linear SVM model with the human-labeled categories and calculated the precision, recall and F1 scores. For the filtering step, we calculated cosine similarity scores to determine the degree of match between resumes and job descriptions. These metrics provided valuable insight into system performance, ensuring efficient identification of the most suitable candidates. The results demonstrate the system's ability to expedite the recruitment process while maintaining a high level of accuracy and precision. Our resume classification and filtering system offers a comprehensive solution to optimize the recruitment process. By using machine learning techniques and similarity algorithms, recruiters and HR professionals can automate the screening process, significantly reducing the time and effort needed to identify top candidates. The system's performance metrics confirm its effectiveness in streamlining the hiring process, enabling recruiters to focus on efficiently evaluating the most qualified applicants."