Project Description: A comprehensive full-stack platform that delivers personalized product recommendations by combining machine learning, uncertainty modeling, and user behavior analysis. This system enhances user experience through dynamic, intelligent suggestions based on real-world data.

Tech Stack: Python, Django, PostgreSQL, React, scikit-learn, Docker

🔗 Preview Version: project.dawidolko.pl

⚠️ Note: This is a static preview version hosted on GitHub Pages. Only the homepage and basic navigation work. Features requiring database connectivity (like shopping cart, user accounts, recommendations) are not functional in this demo version.

💡 For Full Experience: Follow the installation guide below to run the complete application with all features enabled.

The easiest way to run the project is using Docker. This ensures consistent environment across all platforms.

• Docker Desktop installed on your system
• Hardware virtualization enabled (Intel VT-x or AMD-V)

1. Clone the repository:

   git clone https://github.com/dawidolko/SmartRecommender-Project-Django-React
   cd SmartRecommender-Project-Django-React

2. Create environment file:

   # Create .env file in the root directory
   DB_NAME=product_recommendation
   DB_USER=postgres
   DB_PASSWORD=admin
   DB_HOST=db
   DB_PORT=5432
   SECRET_KEY=django-insecure-default-key
   DEBUG=True
   ALLOWED_HOSTS=localhost,127.0.0.1

3. Build and run with Docker:

   docker compose -f .tools/docker/docker-compose.yml up --build

4. Access the application:

   • Frontend (React) → http://localhost:3000
   • Backend (Django) → http://localhost:8000
   • Database (PostgreSQL) → port 5432

# Run in background
docker compose -f .tools/docker/docker-compose.yml up -d --build

# Stop containers
docker compose -f .tools/docker/docker-compose.yml down

# View logs
docker compose -f .tools/docker/docker-compose.yml logs -f

# Enter backend container
docker exec -it SmartRecommender-Django bash

# Enter database
docker exec -it SmartRecommender-PostgreSQL psql -U postgres -d product_recommendation

We provide ready-to-use startup scripts for both Windows and Linux:

# Start backend
cd backend
start.bat

# Start frontend (in a new terminal)
cd frontend
start.bat

# Start backend
cd backend
chmod +x start.sh
./start.sh

# Start frontend (in a new terminal)
cd frontend
chmod +x start.sh
./start.sh

1. Create and activate a virtual environment:

cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install backend dependencies:

pip install -r requirements.txt

3. Configure your PostgreSQL database in .env (see .env.example for template)

4. Apply database migrations and seed data:

python manage.py migrate
python manage.py seed

5. Start the backend server:

python manage.py runserver

Backend will be available at http://127.0.0.1:8000/.

1. Navigate to the frontend directory:

cd frontend

2. Install frontend dependencies:

npm install

3. Start the frontend server:

npm start

Frontend will be available at http://localhost:3000/.

• Advanced Product Recommendations:

  • Collaborative Filtering (User-Based & Item-Based)
  • Content-Based Filtering
  • Association Rules (Frequently Bought Together)
  • Fuzzy Search Logic
  • Sentiment Analysis
  • Probabilistic Methods

• Comprehensive Admin Dashboard:

  • Sales forecasting
  • Demand prediction
  • Customer churn risk assessment
  • Purchase pattern analysis

• User Experience:

  • Personalized product recommendations
  • Smart search with typo tolerance
  • Sentiment-based product discovery

SmartRecommender-Project-Django-React/
├── .database/                  # Database resources
│   ├── entity-relationship-diagram/  # ERD diagrams
│   ├── backup.sql              # Database backup
│   ├── clearAll.sql            # Reset script
│   ├── README.md               # Database documentation
│   ├── RELATIONSHIPS_IN_BASE.md  # Relationship documentation
│   └── tree_database.png       # Visual DB structure
│
├── .docs/                      # Documentation files
│
├── .github/                    # GitHub configuration
│
├── .methods/                   # Algorithm documentation
│   ├── association_rules.md    # Association rules implementation
│   ├── collaborative_filtering.md  # CF algorithm details
│   ├── content_based_filtering.md  # CBF algorithm details
│   ├── fuzzy_search.md         # Fuzzy search implementation
│   ├── probabilistic_methods.md  # Probabilistic methods
│   └── sentiment_analysis.md   # Sentiment analysis details
│
├── .tools/                     # Development tools
│   └── docker/                 # Docker configuration
│       ├── docker-compose.yml  # Docker Compose setup
│       ├── Dockerfile.backend  # Backend container
│       └── Dockerfile.frontend # Frontend container
│
├── backend/                    # Django backend
│   ├── core/                   # Core application
│   ├── home/                   # Main application
│   ├── media/                  # User uploaded files
│   ├── static/                 # Static files
│   ├── venv/                   # Python virtual environment
│   ├── .env                    # Environment variables
│   ├── .env.example            # Environment template
│   ├── check_media.py          # Media verification
│   ├── manage.py               # Django management
│   ├── package.json            # Node.js dependencies
│   ├── requirements.txt        # Python dependencies
│   ├── start.bat               # Windows startup script
│   └── start.sh                # Linux startup script
│
├── frontend/                   # React frontend
│   ├── node_modules/           # Node.js packages
│   ├── public/                 # Public assets
│   ├── src/                    # Source code
│   ├── .env                    # Environment variables
│   ├── .env.example            # Environment template
│   ├── .htaccess               # Apache configuration
│   ├── package-lock.json       # Dependency lock
│   ├── package.json            # Node.js dependencies
│   ├── README.md               # Frontend documentation
│   ├── start.bat               # Windows startup script
│   └── start.sh                # Linux startup script
│
├── images/                     # Project images
│   ├── team1.jpg               # Dawid Olko
│   ├── team2.jpg               # Piotr Smoła
│   └── team3.png               # Dr. Grochowalski
│
├── .gitignore                  # Git ignored files
├── CNAME                       # Custom domain
├── CNAME.md                    # Domain documentation
├── CONTRIBUTING.md             # Contribution guidelines
├── LICENSE                     # License information
└── README.md                   # Main documentation

Our platform implements six distinct recommendation approaches:

1. Collaborative Filtering: Recommends products based on what similar users have purchased
2. Content-Based Filtering: Recommends products with similar attributes to those a user has liked
3. Association Rules: Identifies products frequently bought together using Apriori algorithm
4. Fuzzy Search: Intelligent search with typo tolerance and partial matching
5. Sentiment Analysis: Analyzes customer reviews to recommend positively reviewed products
6. Probabilistic Methods: Predicts user purchase probabilities and product demand

Detailed documentation for each algorithm can be found in the .methods/ directory.

The system uses PostgreSQL with a comprehensive schema of 24 interconnected tables, including:

• Core entities (Users, Products, Categories, Tags)
• E-Commerce functionality (Orders, Cart, Complaints)
• Recommendation tables (Similarities, Interactions, Associations)
• Analytics tables (Sentiment, Purchase Patterns, Risk Assessment)

See .database/ directory for complete database documentation and entity relationship diagrams.

Dawid Olko Creator

Dr. Piotr Grochowalski Supervisor

Piotr Smoła Creator

This project was developed as part of an engineering thesis at TBD University.

This project is licensed under the Apache License 2.0.