Presentation: Reimagining Platform Engagement with Graph Neural Networks

Zalando's shift to graph neural networks for recommendation systems signals a maturation beyond traditional deep learning architectures in recommendation at scale. The real challenge isn't the model itself—it's the data pipeline. Converting sparse user interaction logs into dense heterogeneous graphs requires rethinking how we structure event streaming, handle temporal dynamics, and maintain graph freshness in production. This moves us from treating recommendations as a supervised learning problem to treating them as relational inference problems. For data engineering teams, this means investing in graph databases or specialized graph stores, implementing efficient feature computation pipelines that respect entity relationships, and building monitoring around embedding quality rather than just model accuracy. The broader implication is clear: as competitive advantages narrow, companies must move from optimizing single-entity predictions to optimizing system-wide behavior through relationship intelligence. Your infrastructure investments today should account for graph-native patterns, not retrofit them later.