  APP-installation information is helpful to describe the user's
characteristics. The users with similar APPs installed might share several
common interests and behave similarly in some scenarios. In this work, we learn
a user embedding vector based on each user's APP-installation information.
Since the user APP-installation embedding is learnable without dependency on
the historical intra-APP behavioral data of the user, it complements the
intra-APP embedding learned within each specific APP. Thus, they considerably
help improve the effectiveness of the personalized advertising in each APP, and
they are particularly beneficial for the cold start of the new users in the
APP. In this paper, we formulate the APP-installation user embedding learning
into a bipartite graph embedding problem. The main challenge in learning an
effective APP-installation user embedding is the imbalanced data distribution.
In this case, graph learning tends to be dominated by the popular APPs, which
billions of users have installed. In other words, some niche/specialized APPs
might have a marginal influence on graph learning. To effectively exploit the
valuable information from the niche APPs, we decompose the APP-installation
graph into a set of subgraphs. Each subgraph contains only one APP node and the
users who install the APP. For each mini-batch, we only sample the users from
the same subgraph in the training process. Thus, each APP can be involved in
the training process in a more balanced manner. After integrating the learned
APP-installation user embedding into our online personal advertising platform,
we obtained a considerable boost in CTR, CVR, and revenue.
