Knowledge aware Graph Neural Networks with Label Smoothness Regularization for Recommendation
Knowledge graphs capture structured information and relations between a set of entities or items. As such they represent an attractive source of information that could help improve recommender systems. However existing approaches in this domain rely on manual feature engineering and do not allow for end to end training. Here we propose knowledge aware graph neural networks with label smoothness regularization to provide better recommendations. Conceptually, our approach computes user specific item embeddings by first applying a trainable function that identifies important knowledge graph relationships for a given user. This way we transform the knowledge graph into a user specific weighted graph and then applies a graph neural network to compute personalized item embeddings. To provide better inductive bias, we use label smoothness, which assumes that adjacent items in the knowledge graph are likely to have similar user relevance labels/scores. Label smoothness provides regularization over edge weights and we prove that it is equivalent to a label propagation scheme on a graph. Finally, we combine knowledge aware graph neural networks and label smoothness and present the unifiedmodel. Experiment results show that our method outperforms strong baselines in four datasets. It also achieves strong performance in the scenario where user item interactions are sparse.