GCACL-Rec: A study on conversational recommendation via global context-aware and multi-view contrastive adversarial joint learning

Abstract

Session-based recommendation (SBR) aims to provide personalized recommendations based on anonymous user click sequences. Although existing methods have achieved notable progress, most focus solely on user preferences within a single session, overlooking item transitions across sessions, which limits their ability to model complex behavior patterns. To address this, we propose GCACL-Rec, a model that enhances dynamic modeling by incorporating global item transition information. It constructs a multi-scale graph structure using Multi-scale graph neural networks (MSGNN) and introduces a relative multi-head attention mechanism (RMA) to enhance cross-session dependency modeling. In addition, a multi-view contrastive-adversarial joint learning strategy (MPACL) is adopted to distinguish better relevant from irrelevant information and extract user intent more effectively. During prediction, we use a hybrid structure that combines a neural decision forest (NDF) with the softmax function to enable fine-grained decision optimization and improve feature discrimination and accuracy. Experiments on the Diginetica, Tmall and RetailRocket benchmark datasets show that GCACL-Rec outperforms existing methods, demonstrating clear advantages in cross-session recommendation tasks.