Generating User Privacy-Controllable Synthetic Data for Recommendation Systems
Recommender systems are widely used in e-commerce, news, and advertising, providing personalized recommendations by analyzing user interaction history.However, during large-scale data analysis and sharing, user privacy faces the risk of exposure, especially for users who wish to remain anonymous.While existing synthetic data methods perform well in