About this document

XSimGCL: Simple Graph Contrastive Learning by alexhychen1992 is a document available to read on EtoBox.

The paper introduces XSimGCL, a novel method for contrastive learning in recommendation systems that simplifies the process by eliminating ineffective graph augmentations and employing a noise-based embedding approach. It reveals that contrastive learning enhances recommendation performance by creating more evenly distributed user/item representations, which helps mitigate popularity bias and promote long-tail items. Experimental results demonstrate that XSimGCL outperforms traditional graph augmentation-ba

Author

alexhychen1992

Language

EN