Comparative Analysis of Ad Click Behavior Prediction Using GAN-Augmented Data and Traditional Machine Learning Techniques

In e-commerce, predicting click-through rates (CTR) is crucial to anticipate user behavior. User historical data can be used to extract interests and enhance CTR prediction, leading to higher accuracy. In this study, a Generative Adversarial Network (GAN) has been used to tackle the issue of insufficient dataset for click-through rates. Furthermore, six different machine learning algorithms have been assessed in predicting ad click behavior. For the experimental study, we obtained user demographic and online activity data from Kaggle, along with a binary label indi-cating ad clicks. To enhance the model's performance, we employed a GAN for data augmenta-tion and generated additional training examples. We compared the machine-learning algorithm's outcomes with and without GAN-based data augmentation to evaluate its predicted accuracy. According to the findings, most algorithms have increased sensitivity and specificity after utilis-ing GAN to augment the data, indicating that the generated data has improved their ability to accurately distinguish positive and negative events. GAN-based data augmentation boosted all models to varying degrees, according to the findings.