Long Term User Engagement

Optimizing long-term user engagement (LTE) in online platforms is a crucial research area focusing on maximizing sustained user interaction, rather than just immediate clicks or views. Current research heavily utilizes reinforcement learning (RL) algorithms, often employing techniques like temporal difference learning and actor-critic methods, sometimes within simulator-based frameworks to mitigate the risks of online exploration. These advancements aim to improve recommendation systems and content optimization strategies by considering the downstream effects of choices on user behavior, ultimately leading to more effective and engaging online experiences. The impact extends to improving platform performance metrics like daily active users and overall user satisfaction.