Multiview graph dual-attention deep learning and contrastive learning for multi-criteria recommender systems
Saman Forouzandeh, Pavel N. Krivitsky, Rohitash Chandra
Abstract
Recommender systems leveraging deep learning have significantly improved personalised item suggestions, yet single-criteria models often overlook the nuanced nature of user preferences. Multi-Criteria Recommender Systems (MCRS) address this by modeling multiple aspects (e.g., taste, appearance, location). However, existing deep learning approaches—especially those using shared embeddings or matrix factorization—struggle to capture complex structural and cross-criteria dependencies. To overcome these challenges, we propose a novel framework, D-MGAC (Dual Multiview Graph Attention and Contrastive learning), which formulates MCRS as a multi-edge bipartite graph and applies multiview dual graph attention mechanisms to model both local (per-criterion) and global (cross-criteria) interactions. Furthermore, we define anchor-based contrastive learning in both local and global views to refine the representation quality. Experiments on Yahoo!Movies and BeerAdvocate datasets demonstrate D-MGAC’s superiority, outperforming recent state-of-the-art models.