An Advanced Real-Time Job Recommendation System and Resume Analyser

The increasing complexity and abundance of information in resumes have created confusion and challenges for both candidates and recruiters. An effective resume screening is crucial for students looking for job opportunities as they can demonstrate their skills and qualifications to potential employers and is essential for the recruiter. This study focuses on implementing a resume screening and job recommendation system using Graph Neural Network based on domain adaptation approach and Natural language processing. The goal is to extract latent features from job posts and resumes using GNN and NLP techniques, including Named Entity Recognition (NER). Furthermore, the system leverages the GloVe word embeddings for tokenizing and encoding textual information, enhancing semantic understanding. In addition, the system evaluates the resume score based on the job description using advanced techniques such as cosine similarity and TF-IDF to assess the relevance of keywords and phrases between the resume and job description. By considering the job roles matched to the resume it also recommends the latest jobs posted on different websites using web scraping. In conclusion, this study demonstrates the effectiveness of the GNN-based domain adaptation method can make more accurate in resume screening and job recommendations.