  Graph neural networks (GNNs) have recently emerged as an effective approach
to model neighborhood signals in collaborative filtering. Towards this research
line, graph contrastive learning (GCL) demonstrates robust capabilities to
address the supervision label shortage issue through generating massive
self-supervised signals. Despite its effectiveness, GCL for recommendation
suffers seriously from two main challenges: i) GCL relies on graph augmentation
to generate semantically different views for contrasting, which could
potentially disrupt key information and introduce unwanted noise; ii) current
works for GCL primarily focus on contrasting representations using
sophisticated networks architecture (usually deep) to capture high-order
interactions, which leads to increased computational complexity and suboptimal
training efficiency. To this end, we propose L2CL, a principled Layer-to-Layer
Contrastive Learning framework that contrasts representations from different
layers. By aligning the semantic similarities between different layers, L2CL
enables the learning of complex structural relationships and gets rid of the
noise perturbation in stochastic data augmentation. Surprisingly, we find that
L2CL, using only one-hop contrastive learning paradigm, is able to capture
intrinsic semantic structures and improve the quality of node representation,
leading to a simple yet effective architecture. We also provide theoretical
guarantees for L2CL in minimizing task-irrelevant information. Extensive
experiments on five real-world datasets demonstrate the superiority of our
model over various state-of-the-art collaborative filtering methods. Our code
is available at https://github.com/downeykking/L2CL.
