Graph Neural Networks (GNNs) have significantly enhanced the capability of recommendation systems in e-commerce by leveraging collaborative filtering and session-based methodologies. These advancements have demonstrated improved recommendation accuracy by effectively capturing complex user-item interactions within graph structures. However, the deployment of GNN-based systems at scale encounters substantial challenges, primarily due to their computational intensity and reliance on GPU hardware, which becomes a bottleneck in scenarios dealing with millions of requests per second.

This thesis aims to address the scalability issues inherent in GNNs for e-commerce recommendation systems by exploring knowledge distillation techniques. The objective is to transfer knowledge from complex GNN models to simpler, more computationally efficient representations that can be easily managed and scaled on CPU servers and database environments. This research will involve a comprehensive evaluation of existing knowledge distillation methods applied to GNNs, focusing on their effectiveness in preserving the recommendation quality while significantly reducing computational requirements. A novel methodology for fair comparison of these techniques shall be developed, taking into account factors such as recommendation accuracy, computational efficiency, and scalability.

The outcome of this thesis is expected to provide a systematic approach for making GNN-based recommendation systems more practical for large-scale e-commerce applications, thereby bridging the gap between advanced machine learning models and their real-world applicability in resource-constrained environments.

Requirements: Python, interest in recommendation systems, interest in knowledge distillation

Contact: Lorenz Kummer, Nils Kriege