Click-through rate prediction based on mobile computing and big data analysis

Abstract

This paper designs a click-through rate (CTR) prediction model for ads based on mobile computing of the CTR logs of actual ads. The log preprocessing, feature extraction and model construction were conducted based on big data analysis. To preprocess to logs, an abnormal user detection method was developed based on power-law distribution. Then, the category features were extraction from user, context and ad. Next, the author proposed an evaluation model to predict the CRT of ads based on the extracted features. The experimental results verified the prediction accuracy of our model.