XGBDeepFM for CTR Predictions in Mobile Advertising Benefits from Ad Context

Abstract

The problem of click-through rate (CTR) prediction in mobile advertising is one of the most informative metrics used in mobile business activities, such as profit evaluation and resource management. In mobile advertising, CTR prediction is essential but challenging due to data sparsity. Moreover, existing methods often have difficulty in capturing the different orders of feature interactions simultaneously. In this study, a method was developed to obtain accurate CTR prediction by incorporating contextual features and feature interactions. We initially use extreme gradient boosting (XGBoost) as a feature engineering phase to select highly significant features. The selected features are mobile contextual attributes including time contextual, geography contextual, and other contextual attributes (e.g., weather condition) in actual mobile advertising situations. Our model, XGBoost deep factorization machine- (FM-) supported neutral network (XGBDeepFM), combines the power of XGBoost for feature selection, FM for two-order cross feature interaction, and the deep neural network for high-order feature learning in a united architecture. In a mobile advertising condition, our methods lead to significantly accurate CTR prediction in “wide and deep” type of model. In comparison with existing models, many experiments on commercial datasets show that the XGBDeepFM model has better value of area under curve and improves the effectiveness and efficiency of CTR prediction for mobile advertising.