  Learning effective embedding has been proved to be useful in many real-world
problems, such as recommender systems, search ranking and online advertisement.
However, one of the challenges is data sparsity in learning large-scale item
embedding, as users' historical behavior data are usually lacking or
insufficient in an individual domain. In fact, user's behaviors from different
domains regarding the same items are usually relevant. Therefore, we can learn
complete user behaviors to alleviate the sparsity using complementary
information from correlated domains. It is intuitive to model users' behaviors
using graph, and graph neural networks (GNNs) have recently shown the great
power for representation learning, which can be used to learn item embedding.
However, it is challenging to transfer the information across domains and learn
cross-domain representation using the existing GNNs. To address these
challenges, in this paper, we propose a novel model - Deep Multi-Graph
Embedding (DMGE) to learn cross-domain representation. Specifically, we first
construct a multi-graph based on users' behaviors from different domains, and
then propose a multi-graph neural network to learn cross-domain representation
in an unsupervised manner. Particularly, we present a multiple-gradient descent
optimizer for efficiently training the model. We evaluate our approach on
various large-scale real-world datasets, and the experimental results show that
DMGE outperforms other state-of-art embedding methods in various tasks.
