spacy-job-recommendation
| Property | Value |
|---|---|
| Author | AventIQ-AI |
| Model Type | NLP Recommendation System |
| Accuracy | 85.6% |
| Model URL | https://huggingface.co/AventIQ-AI/spacy-job-recommendation |
What is spacy-job-recommendation?
spacy-job-recommendation is an advanced NLP model designed to bridge the gap between job seekers and employers by providing intelligent job recommendations based on resume analysis. Built on the spaCy framework, it combines cosine similarity measurements with graph-based analysis to deliver highly relevant job matches.
Implementation Details
The model leverages several sophisticated components for job matching: PDF text extraction, skill identification using named entity recognition, and similarity scoring through CountVectorizer and cosine similarity calculations. It implements NetworkX for creating job-role relationship graphs, enabling deeper understanding of skill connections.
- Utilizes spaCy's NLP capabilities for entity recognition
- Implements cosine similarity for matching accuracy
- Features graph-based analysis for skill relationship mapping
- Includes visualization capabilities for top job matches
Core Capabilities
- PDF resume parsing and skill extraction
- Automated job recommendation ranking
- Visual representation of job matches
- Graph-based skill relationship mapping
- High-efficiency matching with 85.6% accuracy
Frequently Asked Questions
Q: What makes this model unique?
The model's combination of NLP-based skill extraction and graph-based relationship mapping sets it apart, providing more contextual and accurate job recommendations compared to traditional keyword-matching systems.
Q: What are the recommended use cases?
The model is ideal for recruitment platforms, career websites, and HR departments looking to automate the initial job matching process. It's particularly effective for medium-sized datasets and can handle various resume formats and job descriptions.
