{"id":29104948,"url":"https://github.com/thatguychandan/adoptimization","last_synced_at":"2026-04-10T11:31:33.308Z","repository":{"id":295554021,"uuid":"990424284","full_name":"ThatGuyChandan/AdOPtimization","owner":"ThatGuyChandan","description":"This project implements an ad optimization system using a hybrid approach combining Thompson Sampling and Upper Confidence Bound (UCB) algorithms. 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Deep learning with attention mechanisms\n  - Thompson Sampling for exploration\n  - Upper Confidence Bound (UCB) for exploitation\n  - Contextual bandits for personalized ad selection\n- **Real-time Optimization**: Dynamic ad selection based on user context and behavior\n- **User Profiling**: Personalized ad delivery based on user interaction history\n- **Interactive Dashboard**: Visualize performance metrics and optimization results\n- **Performance Tracking**: Comprehensive metrics and visualization tools\n\n## Quick Start\n\n1. **Clone the repository**:\n```bash\ngit clone https://github.com/yourusername/efficient_ad_optimization.git\ncd efficient_ad_optimization\n```\n\n2. **Set up virtual environment** (recommended):\n```bash\n# Windows\npython -m venv venv\n.\\venv\\Scripts\\activate\n\n# Linux/Mac\npython -m venv venv\nsource venv/bin/activate\n```\n\n3. **Install dependencies**:\n```bash\npip install -r requirements.txt\n```\n\n4. **Generate sample data**:\n```bash\npython generate_sample_data.py\n```\n\n5. **Run the optimization system**:\n```bash\npython ad_optimizer.py\n```\n\n6. **Launch the dashboard**:\n```bash\nstreamlit run dashboard.py\n```\n\n## How It Works\n\n### 1. Data Processing\n- Loads and preprocesses ad data\n- Normalizes features using StandardScaler\n- Splits data into training and testing sets\n\n### 2. Model Architecture\n- **Feature Extraction**: Deep neural network with attention mechanism\n- **Multi-Objective Heads**: Separate prediction heads for CTR, conversion, and revenue\n- **User Profiling**: Tracks user preferences and interaction history\n- **Context Processing**: Advanced context modeling with self-attention\n\n### 3. Optimization Process\n1. Receives user context and available ads\n2. Uses hybrid approach for ad selection:\n   - Thompson Sampling for exploration\n   - UCB for exploitation\n   - Contextual model for personalization\n3. Shows selected ad to user\n4. Updates model based on observed rewards\n5. Adjusts user profiles and preferences\n\n### 4. Performance Monitoring\n- Tracks multiple metrics:\n  - Click-Through Rate (CTR)\n  - Conversion rate\n  - Revenue\n  - User engagement\n- Generates performance visualizations\n- Saves best performing models\n\n## Dashboard Features\n\nThe Streamlit dashboard provides:\n- Real-time performance metrics\n- Ad selection distribution\n- User engagement analytics\n- Model performance comparisons\n- Interactive visualizations\n\n## Technical Stack\n\n- **Deep Learning**: PyTorch\n- **Data Processing**: Pandas, NumPy\n- **Machine Learning**: scikit-learn\n- **Visualization**: Plotly, Matplotlib, Seaborn\n- **Web Interface**: Streamlit\n- **Progress Tracking**: tqdm\n\n## Requirements\n\nAll required packages are listed in `requirements.txt`. Key dependencies include:\n- Python 3.8+\n- PyTorch 2.2.0\n- Streamlit 1.32.0\n- Pandas 2.2.0\n- NumPy 1.26.3\n- scikit-learn 1.4.0\n\n## Future Improvements\n\n- Integration with real ad platforms\n- Advanced privacy-preserving techniques\n- A/B testing framework\n- Automated hyperparameter optimization\n- Real-time bidding integration\n- Advanced user segmentation\n- Multi-channel attribution modeling\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthatguychandan%2Fadoptimization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthatguychandan%2Fadoptimization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthatguychandan%2Fadoptimization/lists"}