  Collaborative filtering-based recommender systems (RecSys) rely on learning
representations for users and items to predict preferences accurately.
Representation learning on the hypersphere is a promising approach due to its
desirable properties, such as alignment and uniformity. However, the sparsity
issue arises when it encounters RecSys. To address this issue, we propose a
novel approach, graph-based alignment and uniformity (GraphAU), that explicitly
considers high-order connectivities in the user-item bipartite graph. GraphAU
aligns the user/item embedding to the dense vector representations of
high-order neighbors using a neighborhood aggregator, eliminating the need to
compute the burdensome alignment to high-order neighborhoods individually. To
address the discrepancy in alignment losses, GraphAU includes a layer-wise
alignment pooling module to integrate alignment losses layer-wise. Experiments
on four datasets show that GraphAU significantly alleviates the sparsity issue
and achieves state-of-the-art performance. We open-source GraphAU at
https://github.com/YangLiangwei/GraphAU.
