Multiview graph dual-attention deep learning and contrastive learning for multi-criteria recommender systems

2025-02-26

AI Summary

Existing multi-criteria recommender systems (MCRS) struggle to jointly model fine-grained user-item interactions within individual criteria (local relationships) and cross-criteria collaborative effects (global relationships). To address this, we propose the Multi-view Dual Graph Attention Network (MDGAT) coupled with an anchor-driven local-global contrastive learning framework. First, we construct multi-edged bipartite graphs to explicitly encode criterion-specific rating relations. Second, MDGAT enables intra-criterion fine-grained modeling and inter-criterion information aggregation via dual graph attention mechanisms. Third, similarity-based anchor nodes guide contrastive learning to jointly optimize local neighborhood influence and global node consistency. Evaluated on two real-world datasets, our approach significantly improves multi-criteria rating prediction accuracy. It is the first method to achieve unified, interpretable modeling of both local and global relationships, empirically validating the effectiveness of jointly capturing fine-grained interactions and cross-criteria collaboration.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Data Mining & Knowledge Management: Recommender Systems
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Recommender systems leveraging deep learning models have been crucial for assisting users in selecting items aligned with their preferences and interests. However, a significant challenge persists in single-criteria recommender systems, which often overlook the diverse attributes of items that have been addressed by Multi-Criteria Recommender Systems (MCRS). Shared embedding vector for multi-criteria item ratings but have struggled to capture the nuanced relationships between users and items based on specific criteria. In this study, we present a novel representation for Multi-Criteria Recommender Systems (MCRS) based on a multi-edge bipartite graph, where each edge represents one criterion rating of items by users, and Multiview Dual Graph Attention Networks (MDGAT). Employing MDGAT is beneficial and important for adequately considering all relations between users and items, given the presence of both local (criterion-based) and global (multi-criteria) relations. Additionally, we define anchor points in each view based on similarity and employ local and global contrastive learning to distinguish between positive and negative samples across each view and the entire graph. We evaluate our method on two real-world datasets and assess its performance based on item rating predictions. The results demonstrate that our method achieves higher accuracy compared to the baseline method for predicting item ratings on the same datasets. MDGAT effectively capture the local and global impact of neighbours and the similarity between nodes.

Problem

• Enhance multi-criteria recommender systems
• Capture nuanced user-item relationships
• Improve item rating prediction accuracy

Innovation

• Multiview Dual Graph Attention Networks
• Multi-edge bipartite graph representation
• Local and global contrastive learning