Knowledge graphs capture interlinked information between entities and they represent an attractive source of structured information that can be harnessed for recommender systems. However, existing recommender engines use knowledge graphs by manually designing features, do not allow for end-to-end training, or provide poor scalability. Here we propose Knowledge Graph Convolutional Networks (KGCN), an end-to-end trainable framework that harnesses item relationships captured by the knowledge graph to provide better recommendations. Conceptually, KGCN computes user-specific item embeddings by first applying a trainable function that identifies important knowledge graph relations for a given user and then transforming the knowledge graph into a user-specific weighted graph. Then, KGCN applies a graph convolutional neural network that computes an embedding of an item node by propagating and aggregating knowledge graph neighborhood information. Moreover, to provide better inductive bias KGCN uses label smoothness (LS), which provides regularization over edge weights and we prove that it is equivalent to label propagation scheme on a graph. Finally, We unify KGCN and LS regularization, and present a scalable minibatch implementation for KGCN-LS model. Experiments show that KGCN-LS outperforms strong baselines in four datasets. KGCN-LS also achieves great performance in sparse scenarios and is highly scalable with respect to the knowledge graph size.

本研究介绍了一种称为KGNN-LS的新方法，它使用知识图谱和图神经网络来提高推荐系统的准确性, 通过标签平滑正则化（Label Smoothness regularization）在个性化图上计算嵌入，以提供更好的推荐。研究实验证明该方法超越了现有的基线算法。

具有标签平滑正则化的知识感知图神经网络用于推荐系统