Development of Methodology of Graph Neural Networks Application in Recommender Systems

Recommender systems are essential for tailoring user experiences on online platforms, mostly depending on user-content interactions to enhance engagement metrics like click-through rate (CTR). Conventional recommendation models frequently fail to identify higher-order relationships and intricate user behavior patterns, constraining their capacity to deliver precise and varied recommendations. This study examines the utilization of Graph Neural Networks (GNNs) to improve content ranking systems through the generation of task-specific user and content embeddings.