Kaiqiao Zhan

Short-video recommender systems typically optimize ranking models using dense user behavioral signals, such as clicks and watch time. However, these signals are only indirect proxies of user satisfaction and often suffer from noise and bias. Recently, explicit satisfaction feedback collected thro...

While deep learning-based approaches dominate the research on collaborative filtering (CF) in recommender systems, recent studies have re-examined this paradigm and found that deep models do not consistently outperform traditional CF alternatives on standard evaluation metrics. Notably, similarit...

Shared-account usage is common on short-video platforms, especially on mobile and tablet devices, where a single device is accessed by multiple users. While existing industrial solutions generally focus on behavior sequence purification to disentangle mixed user preferences, such approaches inher...

Short-video recommendation systems typically optimize for user satisfaction. However, allocating exposure to creators at critical growth stages incentivizes long-term content supply despite compromising immediate user engagement. Existing efforts concerning creator interests aim at either improvi...