  Graph recommender (GR) is a type of graph neural network (GNNs) encoder that
is customized for extracting information from the user-item interaction graph.
Due to its strong performance on the recommendation task, GR has gained
significant attention recently. Graph contrastive learning (GCL) is also a
popular research direction that aims to learn, often unsupervised, GNNs with
certain contrastive objectives. As a general graph representation learning
method, GCLs have been widely adopted with the supervised recommendation loss
for joint training of GRs. Despite the intersection of GR and GCL research,
theoretical understanding of the relationship between the two fields is
surprisingly sparse. This vacancy inevitably leads to inefficient scientific
research.
  In this paper, we aim to bridge the gap between the field of GR and GCL from
the perspective of encoders and loss functions. With mild assumptions, we
theoretically show an astonishing fact that graph recommender is equivalent to
a commonly-used single-view graph contrastive model. Specifically, we find that
(1) the classic encoder in GR is essentially a linear graph convolutional
network with one-hot inputs, and (2) the loss function in GR is well bounded by
a single-view GCL loss with certain hyperparameters. The first observation
enables us to explain crucial designs of GR models, e.g., the removal of
self-loop and nonlinearity. And the second finding can easily prompt many
cross-field research directions. We empirically show a remarkable result that
the recommendation loss and the GCL loss can be used interchangeably. The fact
that we can train GR models solely with the GCL loss is particularly
insightful, since before this work, GCLs were typically viewed as unsupervised
methods that need fine-tuning. We also discuss some potential future works
inspired by our theory.
