Existing network information flow advertising mostly relies on user behavior data, which is easy to cause user privacy leakage and low precision of advertising delivery. This article combines user behavior data encryption and deep learning technology to propose a method for optimizing the effect of network information flow advertising. The user behavior data is encrypted using Paillier homomorphic encryption technology. Then, the stacked auto encoder (SAE) is used to extract high-order nonlinear features, and the shallow features are automatically combined based on Field-aware Factorization Machines (FFM) to construct a SAE-FFM comprehensive model. At the same time, dropout is used to reduce model overfitting, and the advertising effect is optimized through two-stage pre-training and fine-tuning. The experimental results show that when the number of impressions is 10,000, the average advertising click through rate and conversion rate of the SAE-FFM model are 4.50% and 0.59% respectively, and the information leakage rate is 0.052%. The results show that the SAE-FFM model can greatly improve the click through rate and conversion rate of advertisements, prevent privacy leakage, and help the development of the information flow advertising industry.
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