DimCL: Dimension-Aware Augmentation in Contrastive Learning for Recommendation

Contrastive learning (CL) has achieved remarkable success in addressing data sparsity issues in collaborative filtering (CF) for recommender systems (RSs). The key principle is to generate different augmented views given a user-item interaction graph. However, prior endeavors mainly focus on performing augmentation via stochastic functions, e.g., by injecting perturbations into different hidden dimensions uniformly. Without fine control, the hidden representations of augmentations may contain noisy dimensions that are harmful to CL and irrelevant to RSs. Removing dimension-specific noise is a challenging task due to the following two major bottlenecks. It is difficult to (i) distinguish different dimensions' efficacy for CL and (ii) bridge the semantic gap between CL and RSs. Overlooking these limitations may cause redundant, false-positive, and irrelevant noise in hidden dimensions of the augmented views.