  Given a set of candidate entities (e.g. movie titles), the ability to
identify similar entities is a core capability of many recommender systems.
Most often this is achieved by collaborative filtering approaches, i.e. if
users co-engage with a pair of entities frequently enough, the embeddings
should be similar. However, relying on co-engagement data alone can result in
lower-quality embeddings for new and unpopular entities. We study this problem
in the context recommender systems at Netflix. We observe that there is
abundant semantic information such as genre, content maturity level, themes,
etc. that complements co-engagement signals and provides interpretability in
similarity models. To learn entity similarities from both data sources
holistically, we propose a novel graph-based approach called SemanticGNN.
SemanticGNN models entities, semantic concepts, collaborative edges, and
semantic edges within a large-scale knowledge graph and conducts representation
learning over it. Our key technical contributions are twofold: (1) we develop a
novel relation-aware attention graph neural network (GNN) to handle the
imbalanced distribution of relation types in our graph; (2) to handle web-scale
graph data that has millions of nodes and billions of edges, we develop a novel
distributed graph training paradigm. The proposed model is successfully
deployed within Netflix and empirical experiments indicate it yields up to 35%
improvement in performance on similarity judgment tasks.
