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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.

• 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.

• 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

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

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

1. Clone the repository:

   git clone https://github.com/yourusername/Nexas-Insights.gitcd Nexas-Insights

2. Create and activate a virtual environment:

   # On Unix or MacOSpython -m venv venvsource venv/bin/activate# On Windowspython -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

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

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

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)

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

{"paper_id":"string","title":"string","abstract":"string","authors": ["string"],"keywords": ["string"],"publication_date":"string","field_of_study":"string"}

Editconfig.py to customize:

• API settings
• Database configuration
• Matching algorithm parameters
• Recommendation thresholds
• Logging settings

Run the test suite:

pytest tests/

Generate coverage report:

pytest --cov=. tests/

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.

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

• 0.2.0:

  • Enhanced matching algorithm.
  • Added API authentication.
  • Performance improvements.

• 0.1.0:

  • Initial release.