Oct. 25, 2022, 1:12 a.m. | Feifan Li, Lun Du, Qiang Fu, Shi Han, Yushu Du, Guangming Lu, Zi Li

cs.LG updates on arXiv.org arxiv.org

User engagement prediction plays a critical role for designing interaction
strategies to grow user engagement and increase revenue in online social
platforms. Through the in-depth analysis of the real-world data from the
world's largest professional social platforms, i.e., LinkedIn, we find that
users expose diverse engagement patterns, and a major reason for the
differences in user engagement patterns is that users have different intents.
That is, people have different intents when using LinkedIn, e.g., applying for
jobs, building connections, or …

arxiv engagement forecasting meta network platforms social user engagement

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

DevOps Engineer (Data Team)

@ Reward Gateway | Sofia/Plovdiv