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--- |
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title: HRHUB |
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emoji: 💼 |
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colorFrom: green |
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colorTo: blue |
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sdk: streamlit |
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sdk_version: "1.34.0" |
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app_file: app.py |
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pinned: true |
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--- |
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# 🏢 HRHUB - HR Matching System |
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**Bilateral Matching Engine for Candidates & Companies** |
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A professional HR matching system using NLP embeddings and cosine similarity to connect job candidates with relevant companies based on skills, experience, and requirements. |
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--- |
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HRHUB solves a fundamental inefficiency in hiring: candidates and companies use different vocabularies when describing skills and requirements. Our system bridges this gap using **job postings** as a translator, enriching company profiles to speak the same "skills language" as candidates. |
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### Key Innovation |
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- **Candidates** describe: "Python, Machine Learning, Data Science" |
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- **Companies** describe: "Tech company, innovation, growth" |
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- **Job Postings** translate: "We need Python, AWS, TensorFlow" |
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- **Result**: Accurate matching in the same embedding space ℝ³⁸⁴ |
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--- |
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## 🚀 Features |
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- ✅ **Bilateral Matching**: Both candidates and companies get matched recommendations |
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- ✅ **NLP-Powered**: Uses sentence transformers for semantic understanding |
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- ✅ **Interactive Visualization**: Network graphs showing match connections |
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- ✅ **Scalable**: Handles 9,544 candidates × 180,000 companies |
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- ✅ **Real-time**: Fast similarity computation using cosine similarity |
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- ✅ **Professional UI**: Clean Streamlit interface |
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--- |
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## 📁 Project Structure |
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``` |
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hrhub/ |
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├── app.py # Main Streamlit application |
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├── config.py # Configuration settings |
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├── requirements.txt # Python dependencies |
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├── README.md # This file |
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├── data/ |
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│ ├── mock_data.py # Demo data (MVP) |
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│ ├── data_loader.py # Real data loader (future) |
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│ └── embeddings/ # Saved embeddings (future) |
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├── utils/ |
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│ ├── matching.py # Cosine similarity algorithms |
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│ ├── visualization.py # Network graph generation |
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│ └── display.py # UI components |
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└── assets/ |
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└── style.css # Custom CSS (optional) |
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``` |
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--- |
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## 🛠️ Installation & Setup |
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### Prerequisites |
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- Python 3.8+ |
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- pip package manager |
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- Git |
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### Local Development |
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1. **Clone the repository** |
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```bash |
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git clone https://github.com/your-username/hrhub.git |
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cd hrhub |
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``` |
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2. **Create virtual environment** (recommended) |
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```bash |
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python -m venv venv |
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source venv/bin/activate # On Windows: venv\Scripts\activate |
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``` |
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3. **Install dependencies** |
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```bash |
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pip install -r requirements.txt |
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``` |
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4. **Run the app** |
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```bash |
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streamlit run app.py |
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``` |
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5. **Open browser** |
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Navigate to `http://localhost:8501` |
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--- |
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## 🌐 Deployment (Streamlit Cloud) |
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### Step 1: Push to GitHub |
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```bash |
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git add . |
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git commit -m "Initial commit" |
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git push origin main |
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``` |
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### Step 2: Deploy on Streamlit Cloud |
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1. Go to [share.streamlit.io](https://share.streamlit.io) |
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2. Sign in with GitHub |
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3. Click "New app" |
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4. Select your repository: `hrhub` |
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5. Main file path: `app.py` |
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6. Click "Deploy" |
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**That's it!** Your app will be live at `https://your-app.streamlit.app` |
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--- |
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## 📊 Data Pipeline |
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### Current (MVP - Hardcoded) |
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``` |
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mock_data.py → app.py → Display |
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``` |
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### Future (Production) |
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``` |
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CSV Files → Data Processing → Embeddings → Saved Files |
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↓ |
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app.py loads embeddings → Real-time matching |
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``` |
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### Files to Generate (Next Phase) |
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```python |
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# After running your main code, save these: |
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1. candidate_embeddings.npy # 9,544 × 384 array |
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2. company_embeddings.npy # 180,000 × 384 array |
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3. candidates_processed.pkl # Full candidate data |
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4. companies_processed.pkl # Full company data |
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``` |
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--- |
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## 🔄 Switching from Mock to Real Data |
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### Current Code (MVP) |
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```python |
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# app.py |
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from data.mock_data import get_candidate_data, get_company_matches |
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``` |
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### After Generating Embeddings |
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```python |
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# app.py |
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from data.data_loader import get_candidate_data, get_company_matches |
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``` |
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**That's it!** No other code changes needed. The UI stays the same. |
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--- |
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## 🎨 Configuration |
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Edit `config.py` to customize: |
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```python |
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# Matching Settings |
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DEFAULT_TOP_K = 10 # Number of matches to show |
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MIN_SIMILARITY_SCORE = 0.5 # Minimum score threshold |
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EMBEDDING_DIMENSION = 384 # Vector dimension |
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# UI Settings |
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NETWORK_GRAPH_HEIGHT = 600 # Graph height in pixels |
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# Demo Mode |
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DEMO_MODE = True # Set False for production |
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``` |
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--- |
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## 📈 Technical Details |
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### Algorithm |
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1. **Text Representation**: Convert candidate/company data to structured text |
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2. **Embedding**: Use sentence transformers (`all-MiniLM-L6-v2`) |
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3. **Similarity**: Compute cosine similarity between vectors |
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4. **Ranking**: Sort by similarity score, return top K |
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### Why Cosine Similarity? |
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- ✅ **Scale-invariant**: Focuses on direction, not magnitude |
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- ✅ **Profile shape matching**: Captures proportional skill distributions |
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- ✅ **Fast computation**: Optimized for large-scale matching |
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- ✅ **Proven in NLP**: Standard metric for semantic similarity |
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### Performance |
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- **Loading time**: < 5 seconds (with pre-computed embeddings) |
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- **Matching speed**: < 1 second for 180K companies |
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- **Memory usage**: ~500MB (embeddings loaded) |
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--- |
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## 🧪 Testing |
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### Test Mock Data |
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```bash |
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cd hrhub |
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python data/mock_data.py |
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``` |
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Expected output: |
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``` |
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✅ Candidate: Demo Candidate #0 |
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✅ Top 5 matches loaded |
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✅ Graph data: 6 nodes, 5 edges |
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``` |
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### Test Streamlit App |
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```bash |
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streamlit run app.py |
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``` |
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--- |
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## 🎯 Roadmap |
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### ✅ Phase 1: MVP (Current) |
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- [x] Basic matching logic |
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- [x] Streamlit UI |
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- [x] Network visualization |
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- [x] Hardcoded demo data |
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### 🔄 Phase 2: Production (Next) |
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- [ ] Generate real embeddings |
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- [ ] Load embeddings from files |
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- [ ] Dynamic candidate selection |
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- [ ] Search functionality |
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### 🚀 Phase 3: Advanced (Future) |
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- [ ] User authentication |
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- [ ] Company login view |
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- [ ] Weighted matching (different dimensions) |
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- [ ] RAG-powered recommendations |
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- [ ] Email notifications |
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- [ ] Analytics dashboard |
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--- |
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## 👥 Team |
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**Master's in Business Data Science - Aalborg University** |
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- Roger - Project Lead & Deployment |
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- Eskil - [Role] |
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- [Team Member 3] - [Role] |
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- [Team Member 4] - [Role] |
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--- |
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## 📝 License |
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This project is part of an academic course at Aalborg University. |
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--- |
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## 🤝 Contributing |
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This is an academic project. Contributions are welcome after project submission (December 14, 2024). |
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--- |
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## 📧 Contact |
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For questions or feedback: |
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- Create an issue on GitHub |
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- Contact via Moodle course forum |
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--- |
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## 🙏 Acknowledgments |
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- **Sentence Transformers**: Hugging Face team |
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- **Streamlit**: Amazing framework for data apps |
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- **PyVis**: Interactive network visualization |
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- **Course Instructors**: For guidance and support |
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--- |
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**Last Updated**: December 2024 |
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**Status**: 🟢 Active Development |
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