CGSNet: Contrastive Graph Self-Attention Network for Session-based Recommendation

The goal of session-based recommendation (SBR) is to predict the next item at a certain point in time for anonymous users. Previous methods usually learn session representations based on the item prediction loss, and session-based data consists of limited users’ short-term interactions. Consequently, the model trained with this loss often suffers from data