Enhancing Resume Parsing and Job Recommendation Systems with Attention-Based Neural Networks and Collaborative Filtering

By fusing Attention-Based Neural Networks (ABNN) with Collaborative Filtering (CF) approaches, this study offers a novel way to enhance resume parsing and job systems for recommendations. Improving the precision and effectiveness of resume processing and connecting applicants with appropriate job openings is the main objective. Using attention processes to highlight pertinent information and enhance context understanding, the Attention-based Neural Network extracts important experiences, abilities, and certifications from documents. Meanwhile, Collaborative Filtering makes customized job opening recommendations based on customer preferences and past application history. By combining contextual information with tailored recommendations, this hybrid strategy not only improves the parsing process but also maximizes job recommendations. As a highly effective and scalable answer for contemporary employment markets, the proposed approach seeks to decrease job placement mismatches, boost customer happiness, and expedite the hiring process for both employers and job applicants.