  Graph Convolutional Networks (GCN) can efficiently integrate graph structure
and node features to learn high-quality node embeddings. These embeddings can
then be used for several tasks such as recommendation and search. At Pinterest,
we have developed and deployed PinSage, a data-efficient GCN that learns pin
embeddings from the Pin-Board graph. The Pin-Board graph contains pin and board
entities and the graph captures the pin belongs to a board interaction.
However, there exist several entities at Pinterest such as users, idea pins,
creators, and there exist heterogeneous interactions among these entities such
as add-to-cart, follow, long-click.
  In this work, we show that training deep learning models on graphs that
captures these diverse interactions would result in learning higher-quality pin
embeddings than training PinSage on only the Pin-Board graph. To that end, we
model the diverse entities and their diverse interactions through multiple
bipartite graphs and propose a novel data-efficient MultiBiSage model.
MultiBiSage can capture the graph structure of multiple bipartite graphs to
learn high-quality pin embeddings. We take this pragmatic approach as it allows
us to utilize the existing infrastructure developed at Pinterest -- such as
Pixie system that can perform optimized random-walks on billion node graphs,
along with existing training and deployment workflows. We train MultiBiSage on
six bipartite graphs including our Pin-Board graph. Our offline metrics show
that MultiBiSage significantly outperforms the deployed latest version of
PinSage on multiple user engagement metrics.
