  Knowledge graphs capture structured information and relations between a set
of entities or items. As such knowledge graphs 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 an end-to-end training. Here we propose Knowledge-aware Graph Neural
Networks with Label Smoothness regularization (KGNN-LS) 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 apply a graph
neural network to compute personalized item embeddings. To provide better
inductive bias, we rely on label smoothness assumption, which posits that
adjacent items in the knowledge graph are likely to have similar user relevance
labels/scores. Label smoothness provides regularization over the edge weights
and we prove that it is equivalent to a label propagation scheme on a graph. We
also develop an efficient implementation that shows strong scalability with
respect to the knowledge graph size. Experiments on four datasets show that our
method outperforms state of the art baselines. KGNN-LS also achieves strong
performance in cold-start scenarios where user-item interactions are sparse.
