.png){kind=icon}
.png){kind=icon}
Return
Understanding WeChat User Preferences and Wow Diffusion
.png){kind=icon}
OA
AI
DOI：10.1109/TKDE.2021.3064233.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Social networking (online)
Message service
Knowledge engineering
Computer science
Terminology
Instant messaging
IEEE Fellows
Social networks
social influence
information diffusion
user behavior
user modeling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.4
Papers:
6.8K
Citations:
3.2W
Organization
View more organizations