Graph Contrastive Learning on Multi-label Classification for Recommendations

TL;DR

This paper introduces MCGCL, a graph contrastive learning model designed for multi-label classification in recommendation systems, effectively capturing complex user-item relationships and improving recommendation accuracy.

Contribution

The paper proposes a novel contrastive learning framework with dual training stages for enhanced multi-label recommendation on graph-structured data.

Findings

MCGCL outperforms state-of-the-art methods on Amazon Review datasets.

Contrastive learning improves the capture of user-item relationships.

Dual training stages enhance model robustness and recommendation accuracy.

Abstract

In business analysis, providing effective recommendations is essential for enhancing company profits. The utilization of graph-based structures, such as bipartite graphs, has gained popularity for their ability to analyze complex data relationships. Link prediction is crucial for recommending specific items to users. Traditional methods in this area often involve identifying patterns in the graph structure or using representational techniques like graph neural networks (GNNs). However, these approaches encounter difficulties as the volume of data increases. To address these challenges, we propose a model called Graph Contrastive Learning for Multi-label Classification (MCGCL). MCGCL leverages contrastive learning to enhance recommendation effectiveness. The model incorporates two training stages: a main task and a subtask. The main task is holistic user-item graph learning to capture user-item relationships. The homogeneous user-user (item-item) subgraph is constructed to capture user-user and item-item relationships in the subtask. We assessed the performance using real-world datasets from Amazon Reviews in multi-label classification tasks. Comparative experiments with state-of-the-art methods confirm the effectiveness of MCGCL, highlighting its potential for improving recommendation systems.