融合图增强和采样策略的图卷积协同过滤模型

Graph convolution collaborative filtering model combining graph enhancement and sampling strategies

现有的基于图卷积网络(GCNs)的协同过滤(CF)模型存在两大问题,大多数原始图因存在噪声及数据稀疏问题会严重损害模型性能;对于大型用户项目图来说,传统GCN中的显式消息传递减慢了训练时的收敛速度,削弱了模型的训练效率.针对上述2点,提出融合图增强和采样策略的图卷积协同过滤模型(EL-GCCF).图初始化学习模块通过生成2种图结构,综合考虑图中的结构和特征信息,对原始图进行增强,有效缓解了噪声问题.通过多任务的约束图卷积跳过显式的消息传递,利用辅助采样策略有效缓解训练中的过度平滑问题,提高了模型的训练效率.在2个真实数据集上的实验结果表明,EL-GCCF模型的性能优于众多主流模型,并且具有更高的训练效率.

There are two significant problems with existing collaborative filtering(CF) models based on graph convolutional networks(GCNs). Most original graphs have noise and data sparsity problems that can seriously impair the model performance. In addition, for large user project graphs, the explicit message passing in traditional GCNs slows down the convergence speed during training and weakens the training efficiency of the model. A graph convolution collaborative filtering model combing graph enhancement and sampling strategies(EL-GCCF) was proposed to respond to the above two points. In the graph initialization learning module, the structural information and the feature information in the graph were integrated by generating two graph structures. The original graph was enhanced and the noise problem was effectively mitigated. Explicit message passing was skipped because of the multi-task constrained graph convolution. The over-smoothing problem in training was effectively mitigated and the training efficiency of the model was improved by using an auxiliary sampling strategy. Experimental results on two real datasets show that the EL-GCCF model outperforms many mainstream models and has higher training efficiency.