  While effective in recommendation tasks, collaborative filtering (CF)
techniques face the challenge of data sparsity. Researchers have begun
leveraging contrastive learning to introduce additional self-supervised signals
to address this. However, this approach often unintentionally distances the
target user/item from their collaborative neighbors, limiting its efficacy. In
response, we propose a solution that treats the collaborative neighbors of the
anchor node as positive samples within the final objective loss function. This
paper focuses on developing two unique supervised contrastive loss functions
that effectively combine supervision signals with contrastive loss. We analyze
our proposed loss functions through the gradient lens, demonstrating that
different positive samples simultaneously influence updating the anchor node's
embeddings. These samples' impact depends on their similarities to the anchor
node and the negative samples. Using the graph-based collaborative filtering
model as our backbone and following the same data augmentation methods as the
existing contrastive learning model SGL, we effectively enhance the performance
of the recommendation model. Our proposed Neighborhood-Enhanced Supervised
Contrastive Loss (NESCL) model substitutes the contrastive loss function in SGL
with our novel loss function, showing marked performance improvement. On three
real-world datasets, Yelp2018, Gowalla, and Amazon-Book, our model surpasses
the original SGL by 10.09%, 7.09%, and 35.36% on NDCG@20, respectively.
