E-Commerce Recommender Systems Demo

A comprehensive demonstration of various recommender system approaches for e-commerce applications, showcasing practical implementation, novel techniques, and production-ready deployment.

🚀 Features

• Multiple Recommender Approaches: Collaborative filtering, content-based, hybrid models
• Advanced Techniques: Neural collaborative filtering, attention mechanisms, multi-task learning
• Production-Ready: API endpoints, Docker containerization, cloud deployment
• Comprehensive Evaluation: Offline metrics, A/B testing framework, online evaluation
• Real Datasets: Amazon product reviews, MovieLens, synthetic e-commerce data

📁 Project Structure

├── data/                   # Datasets and data processing
├── models/                 # Recommender system implementations
├── evaluation/             # Offline and online evaluation frameworks
├── api/                    # FastAPI REST API
├── deployment/             # Docker and cloud deployment configs
├── notebooks/              # Jupyter notebooks for exploration
├── tests/                  # Unit and integration tests
└── docs/                   # Documentation

🛠️ Technologies Used

• Frameworks: PyTorch, TensorFlow, LightFM, Surprise, Implicit
• MLOps: MLflow, Weights & Biases, DVC
• API: FastAPI, Redis, PostgreSQL
• Deployment: Docker, Kubernetes, AWS/GCP
• Evaluation: RecSys evaluation metrics, A/B testing

🚀 Quick Start

1. Clone and Setup:

   git clone <your-repo>
   cd recommender-systems-demo
   pip install -r requirements.txt

2. Download Data:

   python scripts/download_data.py

3. Train Models:

   python scripts/train_models.py

4. Start API:

   python api/main.py

5. Run Evaluations:

   python evaluation/run_offline_eval.py

📊 Models Implemented

1. Collaborative Filtering

   • Matrix Factorization (SVD, NMF)
   • Neural Collaborative Filtering
   • LightFM (hybrid)

2. Content-Based

   • TF-IDF + Cosine Similarity
   • Neural Content Embeddings

3. Advanced Approaches

   • Multi-Head Attention Recommender
   • Multi-Task Learning
   • Session-Based Recommendations

📈 Evaluation Metrics

• Offline: Precision@K, Recall@K, NDCG, MAP, MRR
• Online: Click-through rate, Conversion rate, Revenue per user
• A/B Testing: Statistical significance testing, lift analysis

☁️ Deployment

• Local: Docker Compose
• Cloud: AWS ECS, GCP Cloud Run, Azure Container Instances
• Monitoring: Prometheus, Grafana, ELK Stack

📚 Documentation

• Model Architecture
• API Reference
• Evaluation Framework
• Deployment Guide

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📄 License

MIT License - see LICENSE file for details