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. W...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10820329/ |