Quoc Viet Hung Nguyen

A new approach to enhance recommendations while keeping user data private.

Analyzing the risks and defenses against poisoning attacks in online recommendations.

Fast-FedUL provides quick data removal methods for federated learning while ensuring privacy.

A new framework improves recommendations while protecting user privacy.

This study assesses lightweight embedding methods for effective recommender systems.

SCALL adjusts embedding sizes for improved recommendations using less memory.

A new framework improves time series forecasting while ensuring data privacy.