A Data Enhancement Method for Click Prediction of Push Ads

This study aims to improve the accuracy of click-through rate prediction for push ads through machine learning methods. Using the dataset released by Tianchi, we synthesized basic user information, ad features and user behavior logs to enrich the training data. In feature selection, we sieve out the fields that have less impact on prediction, while weighting the user behavior data. For the sample imbalance problem, a Random OverSampling strategy is used to ensure the fairness and effectiveness of the model. Through these methods, we significantly improve the model's prediction accuracy of push ad clicking behavior, and provide a practical solution for the optimization of the ad push system.