Design And Implementation Of Click Through Rate Prediction Platform For Display Advertising

As the development of the Internet industry and the increase of the Internet users,the marketing value of Internet advertising will continue to ascent.If we can grab the natural advantages of Internet advertising,try to use the click-through rate prediction to track the user's preference for an advertisement,it will bring multiple benefits such as impoving the profit of advertisers,the income of publisher and the users experience.Therefore,this paper chooses to study the click-through rate prediction of display advertising and a platform is builded finally,which try to provide a complete machine learning solution for the the click-through rate prediction.This paper systematically introduces the design and implementation of the clickthrough rate prediction platform for display advertising.Firstly,the paper shows its research content after analysing the significance and the present development of the click-through rate prediction.Subsequently,analysing the requirement of the platform according to the machine learning solution steps,that is,the platform provides users with the feature engineering,the model training,the model evaluation,the model prediction and the results analysis.Then,the design and implementation of the platform are discussed around the requirement.In this part,the paper focus on the feature engineering and the model training.The platform excavates the effective features in the real service log through the feature engineering,and try to use the least feature to bring the best model prediction.After receiving the output of the feature engineering,the model training begins working.I choose the logistic regression model which is suitable for processing the high-dimensional features,and the factorization machine model which is suitable for processing the more complex features.Meanwhile,these models are integrated with the gradient boosting decision tree model to improve the prediction result.After the initial implementation of the platform,the paper start to test its quality and try to fix all the problems through further optimization and iteration.Finally,it summarizes the content of this paper and looks forward to the improvement direction of the platform.