Register
This session will explore how graph neural networks (GNNs) can help enhance recommender systems and personalisation algorithms for e-commerce and entertainment industries.
Mariia will talk about how GNNs were employed at Zalando (biggest European fashion e-commerce platform) to generate dynamic user and content embeddings, capturing complex relational data beyond traditional static features. By focusing on the relational context within user-content interactions, GNNs have improved the prediction of click-through rates (CTR), leading to more tailored and engaging user experiences. Attendees will gain insights into the architecture of the GNN model, the methodologies for training and integrating graph-based embeddings into existing systems, and the tangible benefits observed.
This session is ideal for professionals interested in cutting-edge recommender systems and the practical applications of GNNs in large-scale digital platforms.
Speaker
Mariia Bulycheva
Senior Applied Scientist @Zalando, Building Highly Personalized Recommender Systems at Large Scale
Mariia Bulycheva is a Senior Applied Scientist at Zalando, where she leads the development of AI-driven personalization systems. With a strong background in mathematics and previous experience in finance, computer vision and robotics, she brings a multidisciplinary approach to machine learning challenges. At Zalando, Mariia recently designed and implemented a novel graph neural network architecture for content recommendation, introducing this methodology for the first time at the company. Her work focuses on ranking multimodal content and optimizing for long-term user engagement, bridging research and real-world impact at scale.
Before focusing on recommender systems, Mariia worked on pricing and demand forecasting at Zalando, delivering scalable machine learning solutions that significantly improved accuracy and business outcomes.
