Understanding WeChat User Preferences and “Wow” Diffusion

WeChat is the largest social instant messaging platform in China with 1.1 billion monthly active users.“Top Stories“ is a kind of novel friend-enhanced recommendation engine in WeChat, in which users can read articles based on both their own and their friends' preferences. Specifically, when a user reads an article by opening it, the “click” behavior is private. Besides, if the user clicks the wow button, (only) her/his direct connections will be aware of this action/preference. Based on the unique billion-scale WeChat data, we aim to understand user preferences and wow diffusion in Top Stories at different levels. We have 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 it, we present a hierarchical graph representation learning based model ProHENE, which is capable of capturing the structured based social observations discovered above. Our experiments show that the proposed method can significantly improve the prediction performance compared with alternative methods.