Denoised Recommendation Model with Collaborative Signal Decoupling
This addresses noise issues in recommendation systems for users and platforms, but it is incremental as it builds on existing denoising and GNN-based methods.
The paper tackles the problem of noise in user-item interaction matrices in collaborative filtering, which weakens collaborative signals, by proposing DRCSD, a GNN-based model that decouples and denoises signals order-wise, achieving statistically significant improvements in recommendation accuracy on three datasets.
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.