A sophisticated real-time bidding (RTB) optimization platform that uses game theory principles to optimize advertising campaigns and maximize ROI.

The project is divided into two main components:

The backend provides the core optimization engine with the following components:

• BidOptimizationService: Core service that uses game theory models to calculate optimal bids
• CompetitorAnalysisService: Analyzes and tracks competitor behavior in RTB auctions
• PredictionService: Predicts click-through rates, conversion rates, and other metrics
• UserProfileService: Manages user profile data for targeting
• CampaignService: Manages ad campaigns and bid responses

The frontend provides a dashboard for campaign management and optimization insights:

• Campaign Performance: View and track campaign metrics
• Bidding Simulator: Simulate bid outcomes based on game theory models
• Competitor Analysis: Analyze competitor bidding behavior
• Game Theory Insights: Get strategic insights based on game theory models

• Game Theory-Based Optimization: Uses Nash Equilibrium models to find optimal bidding strategies
• Competitor Analysis: Tracks and analyzes competitor bidding patterns
• Machine Learning Predictions: Predicts CTR, CVR, and user value
• Real-time Bidding: Supports real-time bidding protocols
• Interactive Dashboard: Visualizes campaign performance and optimization insights

• Java 17 or higher
• Maven 3.6.3 or higher
• Node.js 14+ and npm 7+
• Git

1. Clone the repository: git clone https://github.com/VarnitOS/AdOpt.git
2. Navigate to the backend directory: cd AdOpt/backend
3. Run mvn clean install to build the project
4. Run mvn spring-boot:run to start the server
5. The backend server will be available at http://localhost:8080

Troubleshooting Backend Issues:

• If you encounter Lombok-related errors, ensure your IDE has Lombok plugin installed and annotation processing enabled
• For "Cannot find symbol" errors in model classes, try rebuilding with mvn clean install -U to update dependencies

1. Navigate to the frontend directory: cd ../frontend
2. Run npm install to install dependencies
3. Run npm run dev to start the development server
4. Open http://localhost:3000 in your browser

Troubleshooting Frontend Issues:

• If you encounter module resolution errors, try clearing the Next.js cache with npm run clean
• For dependency-related issues, ensure you're using the correct Node.js version

The repository includes a convenience script to run both frontend and backend:

chmod +x start-dev.sh
./start-dev.sh

The backend exposes the following main API endpoints:

• GET /api/campaigns: Get all campaigns
• GET /api/campaigns/{id}: Get a specific campaign
• POST /api/campaigns: Create a new campaign
• PUT /api/campaigns/{id}: Update a campaign
• POST /api/bid: Generate an optimal bid for a given bid request
• POST /api/bid/{id}/win: Process auction win notification
• POST /api/bid/{id}/loss: Process auction loss notification

• Backend: Java, Spring Boot, Spring Data JPA, H2 Database
• Frontend: Next.js, TypeScript, Material UI, Chart.js
• Machine Learning: DeepLearning4j
• Game Theory: Custom Nash Equilibrium implementation

The backend can be deployed as a standard Spring Boot application:

1. Build the JAR: mvn clean package
2. Run the JAR: java -jar target/adopt-backend-0.1.0.jar

For production deployment, consider using:

• Docker containers
• AWS Elastic Beanstalk
• Heroku Java deployment

The Next.js frontend can be deployed using:

1. Build the production bundle: npm run build
2. Start the production server: npm start

Recommended hosting options:

• Vercel (optimized for Next.js)
• Netlify
• AWS Amplify

AdOpt is currently in active development. The platform provides core functionality for RTB optimization using game theory principles, but several features are still being refined:

• Core bidding optimization engine
• Basic dashboard with campaign metrics
• Competitor analysis visualization
• Advanced machine learning prediction models
• A/B testing framework
• Budget pacing algorithms
• Multi-account management

We welcome contributions to the AdOpt platform! Here's how you can contribute:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit your changes: git commit -m 'Add amazing feature'
4. Push to your branch: git push origin feature/amazing-feature
5. Open a pull request

Please make sure to update tests as appropriate and follow the code style guidelines.

• Additional Game Theory Models: Implement Stackelberg, Bayesian, and other models
• Advanced Machine Learning: Integrate deep learning for prediction tasks
• A/B Testing Framework: Test different bidding strategies
• Budget Pacing: Advanced budget allocation and pacing algorithms

This project is licensed under the MIT License - see the LICENSE file for details.

For questions or feedback, please reach out to the project maintainers or open an issue in the GitHub repository.