RecSys

🎯 Recommender Systems Learning Project

Exploring modern recommendation algorithms through two powerful approaches

📋 Overview

This repository contains implementations and experiments with two cutting-edge approaches to recommender systems:

1. 🧠 TensorFlow Recommenders - Deep learning-based recommendation models
2. 🔗 Link Prediction with PyTorch Geometric - Graph neural network approaches

🚀 Approaches

1. TensorFlow Recommenders (TFRS)

• Collaborative Filtering: User-item interaction modeling
• Content-Based Filtering: Feature-rich recommendation models
• Deep & Wide Models: Combined memorization and generalization
• Sequential Models: Time-aware recommendations

2. Link Prediction with PyTorch Geometric

• Graph Neural Networks: Node embedding learning
• Graph Convolutional Networks (GCN): Localized feature aggregation
• Graph Attention Networks (GAT): Attention-based node interactions
• Knowledge Graph Embeddings: Complex relationship modeling

📁 Project Structure

📦 recommender-systems-learning
│
├── 📂 tensorflow-recommenders/
│   
├── 📂 pytorch-geometric/  
│
├── 📂 datasets/

🛠️ Installation

Prerequisites

• Python 3.8+
• CUDA-compatible GPU (recommended)

Dependencies

# TensorFlow Recommenders
pip install tensorflow-recommenders
pip install tensorflow

# PyTorch Geometric
pip install torch torchvision torchaudio
pip install torch-geometric
pip install pyg-lib torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.0.0+cu118.html

📊 Datasets

• MovieLens: Movie rating predictions
• Amazon Product Data: Product recommendations
• Citation Networks: Academic paper recommendations
• Social Networks: Friend/connection prediction

🎯 Key Learning Objectives

TensorFlow Recommenders

• Build matrix factorization models
• Implement deep neural collaborative filtering
• Create hybrid recommendation systems
• Handle cold start problems
• Optimize for scalability

PyTorch Geometric

• Understand graph-based recommendations
• Implement node embedding techniques
• Build link prediction models
• Explore attention mechanisms in graphs
• Apply to knowledge graph completion

📚 Resources & References

TensorFlow Recommenders

• Official Documentation
• TFX Tutorials
• Research Papers

PyTorch Geometric

• Official Documentation
• Graph ML Papers
• Geometric Deep Learning

🤝 Contributing

Feel free to contribute by:

• Adding new model implementations
• Improving existing code
• Adding comprehensive documentation
• Reporting issues and bugs

🙏 Acknowledgments

• TensorFlow Recommenders team for the amazing framework
• PyTorch Geometric community for graph ML tools
• Open source datasets contributors

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Happy Learning! 🎓
Building the future of personalized recommendations