Contrastive Learning (CL) has shown promising performance in collaborative filtering. The key idea is to generate augmentation-invariant embeddings by maximizing the Mutual Information between different augmented views of the same instance. However, we empirically observe that existing CL models suffer from the \textsl{dimensional collapse} issue, where user/item embeddings only span a low-dimension subspace of the entire feature space. This suppresses other dimensional information and weakens the distinguishability of embeddings. Here we propose a non-contrastive learning objective, named nCL, which explicitly mitigates dimensional collapse of representations in collaborative filtering. Our nCL aims to achieve geometric properties of \textsl{Alignment} and \textsl{Compactness} on the embedding space. In particular, the alignment tries to push together representations of positive-related user-item pairs, while compactness tends to find the optimal coding length of user/item embeddings, subject to a given distortion. More importantly, our nCL does not require data augmentation nor negative sampling during training, making it scalable to large datasets. Experimental results demonstrate the superiority of our nCL.

翻译：对比学习（Contrastive Learning, CL）在协同过滤中展现了有潜力的性能。其核心思想是通过最大化同一实例不同增广视图之间的互信息，生成具有增广不变性的嵌入表示。然而，我们通过实验观察到，现有对比学习模型存在**维度坍缩**问题，即用户/物品嵌入仅覆盖整个特征空间中的低维子空间。这抑制了其他维度信息，并削弱了嵌入的可区分性。本文提出一种非对比学习目标（nCL），旨在显式缓解协同过滤中表征的维度坍缩。我们的nCL目标是在嵌入空间上实现**对齐性**与**紧凑性**的几何特性。具体而言，对齐性试图将正相关的用户-物品对表示推近，而紧凑性则在给定失真下寻求用户/物品嵌入的最优编码长度。更重要的是，我们的nCL在训练过程中既不需要数据增强，也无需负采样，因此可扩展至大规模数据集。实验结果表明了nCL的优越性。