Session Graph

Session graphs represent user interaction data as graphs, where nodes are items and edges represent transitions between items within and across sessions, aiming to improve recommendation systems by understanding user intent and behavior over time. Current research focuses on incorporating temporal information, handling complex logical relationships between items and sessions (e.g., using hypergraphs and transformers), and leveraging global graph structures to capture cross-session relationships and alleviate cold-start problems. These advancements lead to more accurate and context-aware recommendations, impacting e-commerce, conversational AI, and other applications relying on personalized user experiences.