Hansu Gu

FedHUR is a federated recommendation framework for learning hierarchical utility-guided client relations that consistently outperforms existing federated recommendation baselines, demonstrating the effectiveness of hierarchical utility-guided client relation learning.

LLMs are poised to flip the script on personalization, giving users unprecedented control over their data and how it's used across platforms.

A groundbreaking framework reduces false positives in recommendation systems by over 74%, restoring user control and transparency in content curation.

Generative recommendation systems can now adapt to evolving user behavior without catastrophic forgetting, thanks to a novel drift-aware tokenization method that selectively updates item representations.