AI-Powered Academic Paper Matching System

A sophisticated AI-powered system that matches academic papers with researchers based on their interests, expertise, and research background. The system employs natural language processing, machine learning, and semantic analysis to provide highly relevant paper recommendations.

🌟 Key Features

• Intelligent Profile Analysis: Automatically analyzes researcher profiles to understand their interests and expertise
• Semantic Paper Matching: Uses advanced NLP techniques to match papers with researchers
• Personalized Recommendations: Delivers tailored paper suggestions based on individual research profiles
• RESTful API Integration: Easy-to-use API endpoints for seamless integration
• Scalable Architecture: Designed to handle large volumes of papers and users
• Real-time Updates: Dynamic updating of recommendations as new papers are added

🛠️ Technology Stack

• Backend: Python 3.8+
• API Framework: Flask
• ML/NLP: scikit-learn, NLTK, TensorFlow
• Data Processing: pandas, numpy
• Database: SQLite (default), PostgreSQL (optional)
• Testing: pytest
• Documentation: Sphinx

📁 Project Structure

paper-matching-system/ │ ├── api/ # API endpoints and routing │ ├── init.py │ └── routes.py │ ├── models/ # Core matching and recommendation models │ ├── init.py │ ├── profile_analyzer.py │ ├── semantic_matcher.py │ └── recommender.py │ ├── preprocessing/ # Data preprocessing utilities │ ├── init.py │ └── data_preprocessor.py │ ├── utils/ # Helper functions and utilities │ ├── init.py │ └── helpers.py │ ├── data/ # Data storage │ ├── raw/ # Original data files │ └── processed/ # Processed data files │ ├── tests/ # Test suite │ ├── init.py │ ├── test_preprocessor.py │ ├── test_matcher.py │ └── test_api.py │ ├── docs/ # Documentation ├── main.py # Application entry point ├── data_generator.py # Sample data generator ├── requirements.txt # Project dependencies ├── config.py # Configuration settings └── README.md

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• pip package manager
• Virtual environment (recommended)

Installation

1. Clone the repository:

git clone https://github.com/Harshdev098/paper_matching.git
cd paper_matching

2. Create and activate virtual environment:
```bash
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:
```bash
pip install -r requirements.txt

4. Generate sample data:
```bash
python data_generator.py

5. Start the application:
```bash
python main.py

💻 API Usage
Authentication
```python
import requests

API_KEY = "your_api_key"
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

Get Paper Recommendations
```python
# Get recommendations for a specific user
response = requests.get(
    "http://localhost:5000/api/recommend/123",
    headers=headers
)
recommendations = response.json()

Update User Profile
```python
profile_data = {
    "user_id": "123",
    "interests": ["machine learning", "natural language processing"],
    "skills": ["python", "tensorflow"]
}

response = requests.post(
    "http://localhost:5000/api/update_profile",
    json=profile_data,
    headers=headers
)

📊 Data Formats
User Profile Schema
```json
{
    "user_id": "string",
    "name": "string",
    "email": "string",
    "interests": ["string"],
    "skills": ["string"],
    "academic_background": "string",
    "research_experience": "string"
}

Paper Schema
```json
{
    "paper_id": "string",
    "title": "string",
    "abstract": "string",
    "authors": ["string"],
    "keywords": ["string"],
    "publication_date": "string",
    "field_of_study": "string"
}

🔧 Configuration
Edit config.py to customize:

API settings
Database configuration
Matching algorithm parameters
Recommendation thresholds
Logging settings

🧪 Testing
Run the test suite:
```bash
pytest tests/

Generate coverage report:
```bash
pytest --cov=. tests/

🤝 Contributing
Fork the repository
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

Contribution Guidelines
Follow PEP 8 style guide
Add unit tests for new features
Update documentation
Maintain test coverage above 80%

🔄 Version History
0.2.0
   Enhanced matching algorithm
   Added API authentication
   Performance improvements
0.1.0
   Initial Release


This README provides:

1. Clear project overview and features
2. Detailed installation instructions
3. Comprehensive API documentation
4. Data format specifications
5. Testing and contribution guidelines
6. Future development plans
7. Support information
8. Version history

Remember to:
- Replace placeholder URLs and usernames
- Add actual badges
- Update contact information
- Modify features and requirements based on your implementation
- Add specific examples relevant to your system
- Include actual performance metrics and benchmarks
- Update version history as you release new versions

This README serves as both documentation and a project overview, making it easier for users and contributors to understand and use your system.