Simple and Behavior-Driven Augmentation for Recommendation with Rich Collaborative Signals

TL;DR

This paper introduces SCAR, a simple and effective data augmentation method for graph collaborative filtering that leverages collaborative signals to improve recommendation performance without complex augmentation strategies.

Contribution

The paper proposes SCAR, a novel augmentation technique that uses collaborative signals to generate pseudo-interactions, enhancing graph collaborative filtering with simplicity and robustness.

Findings

SCAR outperforms existing CL-based GCF methods on four benchmark datasets.

SCAR demonstrates strong robustness across hyperparameters.

SCAR is particularly effective in sparse data scenarios.

Abstract

Contrastive learning (CL) has been widely used for enhancing the performance of graph collaborative filtering (GCF) for personalized recommendation. Since data augmentation plays a crucial role in the success of CL, previous works have designed augmentation methods to remove noisy interactions between users and items in order to generate effective augmented views. However, the ambiguity in defining ''noisiness'' presents a persistent risk of losing core information and generating unreliable data views, while increasing the overall complexity of augmentation. In this paper, we propose Simple Collaborative Augmentation for Recommendation (SCAR), a novel and intuitive augmentation method designed to maximize the effectiveness of CL for GCF. Instead of removing information, SCAR leverages collaborative signals extracted from user-item interactions to generate pseudo-interactions, which are then either added to or used to replace existing interactions. This results in more robust representations while avoiding the pitfalls of overly complex augmentation modules. We conduct experiments on four benchmark datasets and show that SCAR outperforms previous CL-based GCF methods as well as other state-of-the-art self-supervised learning approaches across key evaluation metrics. SCAR exhibits strong robustness across different hyperparameter settings and is particularly effective in sparse data scenarios.