Recommendation models utilizing Graph Convolutional Networks (GCNs) have achieved state-of-the-art performance, as they can integrate both the node information and the topological structure of the user-item interaction graph. However, these GCN-based recommendation models not only suffer from over-smoothing when stacking too many layers but also bear performance degeneration resulting from the existence of noise in user-item interactions. In this paper, we first identify a recommendation dilemma of over-smoothing and solution collapsing in current GCN-based models. Specifically, these models usually aggregate all layer embeddings for node updating and achieve their best recommendation performance within a few layers because of over-smoothing. Conversely, if we place learnable weights on layer embeddings for node updating, the weight space will always collapse to a fixed point, at which the weighting of the ego layer almost holds all. We propose a layer-refined GCN model, dubbed LayerGCN, that refines layer representations during information propagation and node updating of GCN. Moreover, previous GCN-based recommendation models aggregate all incoming information from neighbors without distinguishing the noise nodes, which deteriorates the recommendation performance. Our model further prunes the edges of the user-item interaction graph following a degree-sensitive probability instead of the uniform distribution. Experimental results show that the proposed model outperforms the state-of-the-art models significantly on four public datasets with fast training convergence. The implementation code of the proposed method is available at https://github.com/enoche/ImRec.
翻译:利用图表革命网络(GCNs)的建议模型已经达到了最新水平,因为这些模型可以将节点更新和最佳建议性能纳入用户-项目互动图的节点信息与表层结构相结合,然而,这些基于GCN的建议模型不仅在堆叠过多层时会受到过度移动的影响,而且还会由于用户-项目互动中存在噪音而导致性能退化。在本文件中,我们首先提出一种建议,即在当前基于GCN的模型中,过度移动和解决方案崩溃的两难困境。具体地说,这些模型通常能够将所有层嵌入节点更新和实现最佳建议性能的几层结合,因为过度移动。相反,如果我们在嵌入层的节点更新时,这些基于GCN的建议模型不仅会遇到过度移动的偏移,而且由于用户-项目互动中存在的噪音,这些重量空间会一直崩溃到一个固定点,而自上层层层层的权重几乎维持全部。我们提议了一个分层的GCN模型,在GCN的信息传播和节点更新过程中可以改进层次的表示。 此外,以前基于GCNN的培训模型的建议模型将所有从用户间快速分析的图像分析结果,然后将所有从用户- 演示分析结果,在用户- 度上,在用户- 度上,在演示式平流流化的模型分析,在演示图图图的平流的平流的平准度上显示,在演示度上显示,在比度上显示的平差分析。