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What is Self-Supervised Graph Learning for Recommendations about?

This document presents a novel approach called Self-supervised Graph Learning (SGL) for improving recommendation systems by enhancing Graph Convolution Networks (GCNs). SGL addresses limitations such as the influence of high-degree nodes and noise in interactions by introducing self-supervised tasks that generate multiple views of nodes and maximize agreement among them. Empirical results demonstrate that SGL significantly enhances recommendation accuracy, particularly for long-tail items, and increases rob

Author

zyhdasb

Language

EN