Research on Improvement of the Click-Through Rate Prediction Model Based on Differential Privacy

Research on Improvement of the Click-Through Rate Prediction Model Based on Differential Privacy

Click-through rate prediction is crucial in network applications such as recommendation systems and online networks. Designing feature extraction schemes to obtain features and modeling users’ click behavior are used to estimate the probability of users clicking on recommended items. The...

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Bibliographic Details
Main Authors: Lei Tian, Lina Ge, Zhe Wang, Guifen Zhang, Chenyang Xu, Xia Qin
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Click-through rate prediction
differential privacy
gradient descent
long short-term memory
Online Access:https://ieeexplore.ieee.org/document/9921220/

Internet

https://ieeexplore.ieee.org/document/9921220/

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