  Graph Collaborative Filtering (GCF), one of the most widely adopted
recommendation system methods, effectively captures intricate relationships
between user and item interactions. Graph Contrastive Learning (GCL) based GCF
has gained significant attention as it leverages self-supervised techniques to
extract valuable signals from real-world scenarios. However, many methods
usually learn the instances of discrimination tasks that involve the
construction of contrastive pairs through random sampling. GCL approaches
suffer from sampling bias issues, where the negatives might have a semantic
structure similar to that of the positives, thus leading to a loss of effective
feature representation. To address these problems, we present the
\underline{Proto}typical contrastive learning through \underline{A}lignment and
\underline{U}niformity for recommendation, which is called \textbf{ProtoAU}.
Specifically, we first propose prototypes (cluster centroids) as a latent space
to ensure consistency across different augmentations from the origin graph,
aiming to eliminate the need for random sampling of contrastive pairs.
Furthermore, the absence of explicit negatives means that directly optimizing
the consistency loss between instance and prototype could easily result in
dimensional collapse issues. Therefore, we propose aligning and maintaining
uniformity in the prototypes of users and items as optimization objectives to
prevent falling into trivial solutions. Finally, we conduct extensive
experiments on four datasets and evaluate their performance on the task of link
prediction. Experimental results demonstrate that the proposed ProtoAU
outperforms other representative methods. The source codes of our proposed
ProtoAU are available at \url{https://github.com/oceanlvr/ProtoAU}.
