Retrieval Augmented Transformers for Explainable CV-Job Matching

This project presents a Retrieval-Augmented Generation (RAG) based CV analyzer for intelligent candidate–job matching. Traditional recruitment systems rely on keyword matching and often fail to understand the actual meaning of CV content. To overcome this, the proposed system uses semantic embeddings to analyze and organize CV data into structured sections. It retrieves the most relevant candidates and ranks them with clear explanations. It will help in making the recruitment process faster and more efficient.

Poster