  WeChat is the largest social instant messaging platform in China, with 1.1
billion monthly active users. "Top Stories" is a novel friend-enhanced
recommendation engine in WeChat, in which users can read articles based on
preferences of both their own and their friends. Specifically, when a user
reads an article by opening it, the "click" behavior is private. Moreover, if
the user clicks the "wow" button, (only) her/his direct connections will be
aware of this action/preference. Based on the unique WeChat data, we aim to
understand user preferences and "wow" diffusion in Top Stories at different
levels. We have made some interesting discoveries. For instance, the "wow"
probability of one user is negatively correlated with the number of connected
components that are formed by her/his active friends, but the click probability
is the opposite. We further study to what extent users' "wow" and click
behavior can be predicted from their social connections. To address this
problem, we present a hierarchical graph representation learning based model
DiffuseGNN, which is capable of capturing the structure-based social
observations discovered above. Our experiments show that the proposed method
can significantly improve the prediction performance compared with alternative
methods.
