Denoised Recommendation Model with Collaborative Signal Decoupling

TL;DR

This paper introduces DRCSD, a GNN-based recommendation model that effectively denoises user-item interactions by decoupling collaborative signals into distinct orders, leading to improved robustness and accuracy in recommendation systems.

Contribution

The paper proposes a novel GNN-based CF model with collaborative signal decoupling and order-wise denoising modules, addressing limitations of existing denoising approaches.

Findings

DRCSD outperforms baseline models in recommendation accuracy.

The model demonstrates superior robustness against unstable interactions.

Extensive experiments validate the effectiveness of the proposed approach.

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.