Zalando Integrates GNN Into Homepage Recommendations
Zalando engineers describe integrating a Graph Neural Network into its homepage recommender to improve click-through rate prediction. They train a GraphSAGE model with PyTorch Geometric on user-content interaction graphs to produce user and content embeddings, then add those embeddings as daily-updated features to the existing tabular production model to avoid full graph inference and mitigate cold-starts.
Key Points
- 1Train GNN embeddings on user-content interaction graphs using GraphSAGE and PyTorch Geometric
- 2Capture higher-order relational signals to improve click-through rate prediction beyond tabular features
- 3Add embeddings to production model for daily-updated features, avoiding costly end-to-end graph inference
Scoring Rationale
Practical production-focused GNN integration guidance and empirical evaluation + limited novelty beyond established graph recommender techniques and architectures.
Sources
Public references used for this report.
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