Dual-Channel Latent Factor Analysis Enhanced Graph Contrastive Learning for Recommendation

TL;DR

This paper introduces LFA-GCL, a novel graph contrastive learning method that uses latent factor analysis to enhance global collaborative signals in recommendation systems, outperforming existing models.

Contribution

The paper proposes a latent factor analysis-enhanced GCL approach that refines the global graph without stochastic augmentation, improving recommendation accuracy.

Findings

LFA-GCL outperforms state-of-the-art models on four datasets.

The method effectively utilizes global collaborative signals.

It avoids noise introduced by stochastic augmentation.

Abstract

Graph Neural Networks (GNNs) are powerful learning methods for recommender systems owing to their robustness in handling complicated user-item interactions. Recently, the integration of contrastive learning with GNNs has demonstrated remarkable performance in recommender systems to handle the issue of highly sparse user-item interaction data. Yet, some available graph contrastive learning (GCL) techniques employ stochastic augmentation, i.e., nodes or edges are randomly perturbed on the user-item bipartite graph to construct contrastive views. Such a stochastic augmentation strategy not only brings noise perturbation but also cannot utilize global collaborative signals effectively. To address it, this study proposes a latent factor analysis (LFA) enhanced GCL approach, named LFA-GCL. Our model exclusively incorporates LFA to implement the unconstrained structural refinement, thereby obtaining an augmented global collaborative graph accurately without introducing noise signals. Experiments on four public datasets show that the proposed LFA-GCL outperforms the state-of-the-art models.