Denoised Recommendation Model with Collaborative Signal Decoupling

• URL: http://arxiv.org/abs/2511.04237v1
• Date: Thu, 06 Nov 2025 10:18:02 GMT
• Title: Denoised Recommendation Model with Collaborative Signal Decoupling
• Authors: Zefeng Li, Ning Yang,
• Abstract summary: Collaborative filtering (CF) algorithm suffers from suboptimal recommendation performance due to noise in user-item interaction matrix.<n>This study proposes a novel GNN-based CF model called DRCSD for denoising unstable interactions.
• Score: 2.7629252298084026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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