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 re

SUBMITTER: Li X 

PROVIDER: S-EPMC12574935 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature