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 (NLP), machine learning (ML), 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/yourusername/Nexas-Insights.git
   cd Nexas-Insights

2. Create and activate a virtual environment:

   # On Unix or MacOS
   python -m venv venv
   source venv/bin/activate
   
   # On Windows
   python -m venv venv
   .\venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Generate sample data (if needed):

   python data_generator.py

5. Start the application:

   python main.py

---

💻 API Usage

Authentication

import requests

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

Get Paper Recommendations

response = requests.get(
    "http://localhost:5000/api/recommend/123",
    headers=headers
)
recommendations = response.json()

Update User Profile

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

{
    "user_id": "string",
    "name": "string",
    "email": "string",
    "interests": ["string"],
    "skills": ["string"],
    "academic_background": "string",
    "research_experience": "string"
}

Paper Schema

{
    "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:

pytest tests/

Generate coverage report:

pytest --cov=. tests/

---

🤝 Contributing

1. Fork the repository.
2. Create your feature branch:

   git checkout -b feature/AmazingFeature

3. Commit your changes:

   git commit -m 'Add some AmazingFeature'

4. Push to the branch:

   git push origin feature/AmazingFeature

5. 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.

---

Project Contributers: