Unveiling the Mechanisms: A Detailed Case Study of Recommendation Algorithms in Advertising Science

In order to realize the privacy protection in the process of advertising recommendation and improve the security of the network, a DeepFM recommendation algorithm (DP-DeepFM) based on differential privacy is proposed. By learning during model training Gaussian noise is added to the Adam optimization algorithm, and the gradient is trimmed to prevent the addition of model performance degradation caused by excessive noise, and it is used for advertising recommendation. Simulation experiments show that the algorithm can ensure the effectiveness of advertising recommendation results while improving the differential privacy protection.