Although the collaborative filtering (CF) algorithm has achieved remarkable performance in recommendation systems, it suffers from suboptimal recommendation performance due to noise in the user-item interaction matrix. Numerous noise-removal studies have improved recommendation models, but most existing approaches conduct denoising on a single graph. This may cause attenuation of collaborative signals: removing edges between two nodes can interrupt paths between other nodes, weakening path-dependent collaborative information. To address these limitations, this study proposes a novel GNN-based CF model called DRCSD for denoising unstable interactions. DRCSD includes two core modules: a collaborative signal decoupling module (decomposes signals into distinct orders by structural characteristics) and an order-wise denoising module (performs targeted denoising on each order). Additionally, the information aggregation mechanism of traditional GNN-based CF models is modified to avoid cross-order signal interference until the final pooling operation. Extensive experiments on three public real-world datasets show that DRCSD has superior robustness against unstable interactions and achieves statistically significant performance improvements in recommendation accuracy metrics compared to state-of-the-art baseline models.

翻译：尽管协同过滤（CF）算法在推荐系统中取得了显著性能，但由于用户-物品交互矩阵中的噪声，其推荐性能仍存在次优问题。大量去噪研究改进了推荐模型，但现有方法大多在单一图上进行去噪。这可能导致协同信号衰减：移除两个节点之间的边可能中断其他节点间的路径，从而削弱依赖路径的协同信息。为解决这些局限性，本研究提出了一种名为DRCSD的新型基于图神经网络的协同过滤模型，用于对不稳定交互进行去噪。DRCSD包含两个核心模块：协同信号解耦模块（通过结构特征将信号分解为不同阶次）和分阶去噪模块（对各阶次进行针对性去噪）。此外，本研究改进了传统基于图神经网络的协同过滤模型的信息聚合机制，以避免交叉阶次信号干扰，直至最终池化操作。在三个公开真实数据集上的大量实验表明，DRCSD对不稳定交互具有优异的鲁棒性，并且在推荐准确性指标上相比最先进的基线模型取得了统计学显著的性能提升。