D2PDRF: A differential privacy recommendation algorithm based on federated learning
D2PDRF combines federated learning with differential privacy and attention mechanisms to improve recommendation accuracy and personalization while protecting user privacy.
Published Jul 11, 20243 citationsPaper ↗
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Abstract
To improve recommendation systems' accuracy and customization while preserving user privacy, this study introduces a federated learning-based dynamic recommendation model, D2PDRF. Utilizing an attention mechanism, the model adjusts the blend of long-term and short-term interests, enhancing system adaptability. By integrating differential privacy with federated learning, user privacy is safeguarded. Tests on public datasets show the model enhances recommendation precision and personalization, ensuring data confidentiality.