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Contrastive Learning (CL) has shown promising performance in collaborativefiltering. The key idea is to generate augmentation-invariant embeddings bymaximizing the Mutual Information between different augmented views of the sameinstance. However, we empirically observe that existing CL models suffer fromthe \textsl{dimensional collapse} issue, where user/item embeddings only span alow-dimension subspace of the entire feature space. This suppresses otherdimensional information and weakens the distinguishability of embeddings. Herewe propose a non-contrastive learning objective, named nCL, which explicitlymitigates 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 triesto push together representations of positive-related user-item pairs, whilecompactness tends to find the optimal coding length of user/item embeddings,subject to a given distortion. More importantly, our nCL does not require dataaugmentation nor negative sampling during training, making it scalable to largedatasets. Experimental results demonstrate the superiority of our nCL.

Towards Mitigating Dimensional Collapse of Representations in Collaborative Filtering