https://github.com/thatguychandan/adoptimization
This project implements an ad optimization system using a hybrid approach combining Thompson Sampling and Upper Confidence Bound (UCB) algorithms. The system learns to select the most effective ads based on user context and historical performance.
https://github.com/thatguychandan/adoptimization
numpy pandas plotly python pytorch reinforcement-learning scikit-learn streamlit thompson-sampling upper-confidence-bound
Last synced: 2 months ago
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This project implements an ad optimization system using a hybrid approach combining Thompson Sampling and Upper Confidence Bound (UCB) algorithms. The system learns to select the most effective ads based on user context and historical performance.
- Host: GitHub
- URL: https://github.com/thatguychandan/adoptimization
- Owner: ThatGuyChandan
- Created: 2025-05-26T05:16:25.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-26T05:27:57.000Z (about 1 year ago)
- Last Synced: 2025-07-27T00:40:47.612Z (11 months ago)
- Topics: numpy, pandas, plotly, python, pytorch, reinforcement-learning, scikit-learn, streamlit, thompson-sampling, upper-confidence-bound
- Language: Python
- Homepage:
- Size: 16.6 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Efficient Ad Optimization System
An advanced ad optimization system that uses reinforcement learning and deep learning to maximize ad performance across multiple objectives (CTR, conversion, and revenue).
## Key Features
- **Multi-Objective Optimization**: Simultaneously optimizes for CTR, conversion rate, and revenue
- **Advanced ML Models**:
- Deep learning with attention mechanisms
- Thompson Sampling for exploration
- Upper Confidence Bound (UCB) for exploitation
- Contextual bandits for personalized ad selection
- **Real-time Optimization**: Dynamic ad selection based on user context and behavior
- **User Profiling**: Personalized ad delivery based on user interaction history
- **Interactive Dashboard**: Visualize performance metrics and optimization results
- **Performance Tracking**: Comprehensive metrics and visualization tools
## Quick Start
1. **Clone the repository**:
```bash
git clone https://github.com/yourusername/efficient_ad_optimization.git
cd efficient_ad_optimization
```
2. **Set up virtual environment** (recommended):
```bash
# Windows
python -m venv venv
.\venv\Scripts\activate
# Linux/Mac
python -m venv venv
source venv/bin/activate
```
3. **Install dependencies**:
```bash
pip install -r requirements.txt
```
4. **Generate sample data**:
```bash
python generate_sample_data.py
```
5. **Run the optimization system**:
```bash
python ad_optimizer.py
```
6. **Launch the dashboard**:
```bash
streamlit run dashboard.py
```
## How It Works
### 1. Data Processing
- Loads and preprocesses ad data
- Normalizes features using StandardScaler
- Splits data into training and testing sets
### 2. Model Architecture
- **Feature Extraction**: Deep neural network with attention mechanism
- **Multi-Objective Heads**: Separate prediction heads for CTR, conversion, and revenue
- **User Profiling**: Tracks user preferences and interaction history
- **Context Processing**: Advanced context modeling with self-attention
### 3. Optimization Process
1. Receives user context and available ads
2. Uses hybrid approach for ad selection:
- Thompson Sampling for exploration
- UCB for exploitation
- Contextual model for personalization
3. Shows selected ad to user
4. Updates model based on observed rewards
5. Adjusts user profiles and preferences
### 4. Performance Monitoring
- Tracks multiple metrics:
- Click-Through Rate (CTR)
- Conversion rate
- Revenue
- User engagement
- Generates performance visualizations
- Saves best performing models
## Dashboard Features
The Streamlit dashboard provides:
- Real-time performance metrics
- Ad selection distribution
- User engagement analytics
- Model performance comparisons
- Interactive visualizations
## Technical Stack
- **Deep Learning**: PyTorch
- **Data Processing**: Pandas, NumPy
- **Machine Learning**: scikit-learn
- **Visualization**: Plotly, Matplotlib, Seaborn
- **Web Interface**: Streamlit
- **Progress Tracking**: tqdm
## Requirements
All required packages are listed in `requirements.txt`. Key dependencies include:
- Python 3.8+
- PyTorch 2.2.0
- Streamlit 1.32.0
- Pandas 2.2.0
- NumPy 1.26.3
- scikit-learn 1.4.0
## Future Improvements
- Integration with real ad platforms
- Advanced privacy-preserving techniques
- A/B testing framework
- Automated hyperparameter optimization
- Real-time bidding integration
- Advanced user segmentation
- Multi-channel attribution modeling