CTR Prediction Model Using xDeepFM and Bayesian optimization

Abstract

With the development of internet, increasing people tends to consume online, which is convenient and timesaving. Taobao is the largest online shopping site in China. In this paper, we use the data from Taobao to predict the click-through-rate (CTR), an important metrics that measures the number of clicks advertisers receive on their ads per number of impressions. We introduce xDeepFM model to predict CTR and use Bayesian optimization to optimize. The xDeepFM model is a combined model, composed by DNN and CIN, like Wide&Deep. Based on it, the xDeepFM model enables to catch features of different dimensions: implicit high-order interactions and explicit high-order interactions. In addition, we use the Bayesian optimization to get the optimal hyperparameters. The metrics we used is Auc Roc, and the higher Auc-Roc is, the better performance the model will gain. The Auc Roc of our modle is 0.651 higher 0.031 and 0.012 respectively than LightGBM and DeepFM.