解耦对比协同过滤（英文版）

英文标题：Disentangled Contrastive Collaborative Filtering中文摘要：本篇研究提出了一种基于图神经网络和图对比学习的协作过滤框架，通过自我监督学习进行自适应增强，实现目的的解耦和噪声抑制，将学习到的解耦表示与全局上下文结合，相比于现有的解决方案，具有更好的性能表现。英文摘要：Recent studies show that graph neural networks (GNNs) are prevalent to modelhigh-order relationships for collaborative filtering (CF). Towards thisresearch line, graph contrastive learning (GCL) has exhibited powerfulperformance in addressing the supervision label shortage issue by learningaugmented user and item representations. While m