The Problem with Black Boxes
In the age of GPT-4, it's tempting to throw every text problem at an LLM. But for resume matching, we often need speed, explainability, and privacy. Uploading personal data to a third-party API isn't always the best choice.
ResumeFit solves this by running completely locally. It parses resumes and job descriptions to provide a clear, quantifiable match score using classical NLP techniques.
The Science: TF-IDF & Cosine Similarity
Instead of complex embeddings, ResumeFit uses TF-IDF (Term Frequency-Inverse Document Frequency).
- Vectorization: It converts the resume and job description into mathematical vectors. Words that are rare but important (like "Django" or "Kubernetes") get higher weights than common words (like "the" or "and").
- Cosine Similarity: It calculates the cosine of the angle between these two vectors.
- A score of 1.0 (0°) means a perfect match.
- A score of 0.0 (90°) means no overlap.
This approach is deterministic and incredibly fast, running in milliseconds.
Application Architecture
The project is built as a robust Django web application:
- Backend: Python & Django 4.2.
- PDF Processing:
PyMuPDF(fitz) for reliable text extraction from uploaded resumes. - NLP Engine:
scikit-learnfor the vectorization and similarity logic. - Database: PostgreSQL for production-grade persistence of analysis history.
Key Features
- Skill Gap Analysis: Beyond just a score, the app extracts skills using regex patterns and explicitly tells you what you're missing compared to the job description.
- Privacy-First: No data leaves your server. Everything is processed logically.
- Modern UI: A clean, glassmorphism-inspired interface provides a premium user experience.
Why "Old School" NLP Works
ResumeFit proves that you don't always need the heavy hammer of Generative AI. For strictly comparing the vocabulary and key terms of two documents, statistical methods like TF-IDF offer a highly efficient and explainable solution that is often "good enough" or even better for objective filtering.