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.

翻译：协同过滤推荐系统（RecSys）依赖于学习用户和物品的表征以准确预测偏好。基于超球面的表征学习因其理想特性（如对齐性和均匀性）而成为一种有前景的方法。然而，当该方法应用于推荐系统时，稀疏性问题随之凸显。为解决该问题，我们提出了一种新颖方法——基于图的对齐与均匀性（GraphAU），该方法显式地考虑了用户-物品二分图中的高阶连通性。GraphAU通过邻域聚合器将用户/物品嵌入与高阶邻居的稠密向量表征对齐，从而避免了分别计算与高阶邻居的繁重对齐过程。针对对齐损失的差异性，GraphAU包含一个逐层对齐池化模块，以整合各层的对齐损失。在四个数据集上的实验表明，GraphAU显著缓解了稀疏性问题，并实现了最先进的性能。我们在https://github.com/YangLiangwei/GraphAU上开源了GraphAU代码。