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

Doyun Choi, Cheonwoo Lee, Jaemin Yoo

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. We conduct experiments on four benchmark datasets and show that SCAR outperforms previous CL-based GCF methods and other state-of-the-art self-supervised approaches across key evaluation metrics, while maintaining robust performance in sparse data scenarios. Our implementation is available at https://github.com/cdy9777/SCAR.

External IDs:dblp:conf/bigdataconf/ChoiLY25