While Graph Convolutional Networks (GCNs) have shown great potential in recommender systems and collaborative filtering (CF), they suffer from expensive computational complexity and poor scalability. On top of that, recent works mostly combine GCNs with other advanced algorithms which further sacrifice model efficiency and scalability. In this work, we unveil the redundancy of existing GCN-based methods in three aspects: (1) Feature redundancy. By reviewing GCNs from a spectral perspective, we show that most spectral graph features are noisy for recommendation, while stacking graph convolution layers can suppress but cannot completely remove the noisy features, which we mostly summarize from our previous work; (2) Structure redundancy. By providing a deep insight into how user/item representations are generated, we show that what makes them distinctive lies in the spectral graph features, while the core idea of GCNs (i.e., neighborhood aggregation) is not the reason making GCNs effective; and (3) Distribution redundancy. Following observations from (1), we further show that the number of required spectral features is closely related to the spectral distribution, where important information tends to be concentrated in more (fewer) spectral features on a flatter (sharper) distribution. To make important information be concentrated in as few features as possible, we sharpen the spectral distribution by increasing the node similarity without changing the original data, thereby reducing the computational cost. To remove these three kinds of redundancies, we propose a Simplified Graph Denoising Encoder (SGDE) only exploiting the top-K singular vectors without explicitly aggregating neighborhood, which significantly reduces the complexity of GCN-based methods. We further propose a scalable contrastive learning framework to alleviate data sparsity and to boost model robustness and generalization, leading to significant improvement. Extensive experiments on three real-world datasets show that our proposed SGDE not only achieves state-of-the-art but also shows higher scalability and efficiency than our previously proposed GDE as well as traditional and GCN-based CF methods.

虽然图卷积网络（GCN）在推荐系统和协同过滤（CF）中显示出巨大的潜力，但它们面临着昂贵的计算复杂性和较差的可扩展性。最重要的是，最近的工作大多将 GCN 与其他高级算法结合起来，这进一步牺牲了模型的效率和可扩展性。在这项工作中，我们从三个方面揭示了现有基于 GCN 的方法的冗余：（1）特征冗余。通过从谱的角度回顾GCN，我们发现大多数谱图特征对于推荐来说都是有噪声的，而堆叠图卷积层可以抑制但不能完全消除噪声特征，这主要是我们从之前的工作中总结的；(2)结构冗余。通过深入了解用户/项目表示的生成方式，我们发现它们的独特之处在于谱图特征，而 GCN 的核心思想（即邻域聚合）并不是 GCN 有效的原因；(3)分布冗余。根据（1）的观察，我们进一步表明所需光谱特征的数量与光谱分布密切相关，其中重要信息往往集中在更平坦（更尖锐）的分布上更多（更少）的光谱特征。为了使重要信息集中在尽可能少的特征中，我们在不改变原始数据的情况下通过增加节点相似度来锐化谱分布，从而降低计算成本。为了消除这三种冗余，我们提出了一种简化图去噪编码器（SGDE），仅利用前K 个奇异向量，而不显式聚合邻域，这显着降低了基于 GCN 的方法的复杂性。我们进一步提出了一个可扩展的对比学习框架，以减轻数据稀疏性并提高模型的鲁棒性和泛化性，从而带来显着的改进。对三个真实世界数据集的大量实验表明，我们提出的 SGDE 不仅达到了最先进的水平，而且比我们之前提出的 GDE 以及传统和基于 GCN 的 CF 方法具有更高的可扩展性和效率。