  Low-dimensional embeddings of knowledge graphs and behavior graphs have
proved remarkably powerful in varieties of tasks, from predicting unobserved
edges between entities to content recommendation. The two types of graphs can
contain distinct and complementary information for the same entities/nodes.
However, previous works focus either on knowledge graph embedding or behavior
graph embedding while few works consider both in a unified way. Here we present
BEM , a Bayesian framework that incorporates the information from knowledge
graphs and behavior graphs. To be more specific, BEM takes as prior the
pre-trained embeddings from the knowledge graph, and integrates them with the
pre-trained embeddings from the behavior graphs via a Bayesian generative
model. BEM is able to mutually refine the embeddings from both sides while
preserving their own topological structures. To show the superiority of our
method, we conduct a range of experiments on three benchmark datasets: node
classification, link prediction, triplet classification on two small datasets
related to Freebase, and item recommendation on a large-scale e-commerce
dataset.
