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

TL;DR

This paper introduces a novel deep learning framework for multi-criteria recommender systems that uses multiview dual graph attention networks and contrastive learning to better capture complex user-item relationships, improving rating prediction accuracy.

Contribution

The study proposes a new representation for MCRS using a multi-edge bipartite graph and introduces MDGAT with contrastive learning to enhance relationship modeling.

Findings

Achieves higher rating prediction accuracy than baseline methods.

Effectively captures local and global relations between users and items.

Demonstrates improved performance on real-world datasets.

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.