• deep learning
• graph neural networks
• machine learning
• personalization
• recommendation systems

We present a system to provide real-time and personalized product recommendations for large e-commerce platforms, specifically focusing on fashion retail. The system aims to achieve accurate and scalable recommendations with minimal response times, ensuring user satisfaction. Extensive experimentation with datasets from H&M’s e-commerce platform demonstrates the system’s effectiveness in forecasting purchase sequences and handling multi-interaction scenarios, achieving efficient recommendation and personalization under real-world constraints.