Recommender systems (RecSys) have become essential in modern society, driving user engagement and satisfaction across diverse online platforms. Most RecSys focuses on designing a powerful encoder to embed users and items into high-dimensional vector representation space, with loss functions optimizing their representation distributions. Recent studies reveal that directly optimizing key properties of the representation distribution, such as alignment and uniformity, can outperform complex encoder designs. However, existing methods for optimizing critical attributes overlook the impact of dataset sparsity on the model: limited user-item interactions lead to sparse alignment, while excessive interactions result in uneven uniformity, both of which degrade performance. In this paper, we identify the sparse alignment and uneven uniformity issues, and further propose Regularized Alignment and Uniformity (RAU) to cope with these two issues accordingly. RAU consists of two novel regularization methods for alignment and uniformity to learn better user/item representation. 1) Center-strengthened alignment further aligns the average in-batch user/item representation to provide an enhanced alignment signal and further minimize the disparity between user and item representation. 2) Low-variance-guided uniformity minimizes the variance of pairwise distances along with uniformity, which provides extra guidance to a more stabilized uniformity increase during training. We conducted extensive experiments on three real-world datasets, and the proposed RAU resulted in significant performance improvements compared to current state-of-the-art CF methods, which confirms the advantages of the two proposed regularization methods.

翻译：推荐系统（RecSys）在现代社会中已变得不可或缺，它在各类在线平台上驱动着用户参与度与满意度。大多数推荐系统侧重于设计强大的编码器，将用户和物品嵌入高维向量表示空间，并通过损失函数优化其表示分布。近期研究表明，直接优化表示分布的关键属性（如对齐性和均匀性）能够超越复杂的编码器设计。然而，现有优化关键属性的方法忽略了数据集稀疏性对模型的影响：有限的用户-物品交互会导致稀疏对齐，而过度的交互则会引起不均匀的均匀性，这两者均会降低模型性能。本文中，我们识别出稀疏对齐与不均匀均匀性问题，并进一步提出正则化对齐与均匀性（RAU）以分别应对这两个问题。RAU包含两种针对对齐性和均匀性的新型正则化方法，以学习更优的用户/物品表示。1）中心增强对齐进一步对齐批次内用户/物品表示的平均值，以提供增强的对齐信号，并进一步缩小用户与物品表示之间的差异。2）低方差引导均匀性在优化均匀性的同时最小化成对距离的方差，从而为训练过程中更稳定的均匀性提升提供额外指导。我们在三个真实数据集上进行了大量实验，结果表明所提出的RAU相较于当前最先进的协同过滤方法取得了显著的性能提升，这证实了两种正则化方法的优势。