Graph contrastive learning view construction methods in recommender systems: a survey

Abstract

Recent advances in deep learning have significantly improved recommendation systems. However, these methods often rely heavily on labeled data, leaving challenges like data sparsity and the cold-start problem unresolved. Self-supervised learning, particularly Graph Contrastive Learning (GCL), has emerged as a powerful approach to mitigate these issues by generating informative views from unlabeled data, attracting considerable attention in recent years. This survey provides a timely and comprehensive review of current GCL-based recommendation methods. First, it introduces a comprehensive framework and taxonomy for view construction in GCL for recommendation systems, dividing it into three main types: structure generation, feature generation, and modality generation. Each category is analyzed in detail, offering insights into their methodologies, strengths, and limitations. Comparative experiments and visualization experiments are conducted on three public datasets, analyzing the complexity of various methods to guide the selection of appropriate approaches. The survey also highlights existing limitations and proposes future research directions along with potential roadmaps to inspire innovative solutions in recommendation systems.

Graphical abstract

Keywords

contrastive learning / view generation / recommendation / data augmentation

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