Hierarchical attention and feature projection for click-through rate prediction

Click-through rate (CTR) prediction plays an important role in many industrial applications, feature engineering directly influences CTR prediction performance because features are normally the multi-field type. However, the existing CTR prediction techniques either neglect the importance of each feature or regard the feature interactions equally for feature learning. In addition, using an inner product or a Hadamard product is too simple to effectively model the feature interactions. These limitations lead to suboptimal performances of existing models. In this paper, we propose a framework called Hierarchical Attention and Feature Projection neural network (HAFP) for CTR prediction, which enables the automatically learning of more representative and efficient feature representation in an end-to-end manner. Towards this end, we employ a feature learning layer with a hierarchical attention mechanism to jointly extract more generalized and dominant features and feature interactions. In addition, a projective bilinear function is designed in meaningful second-order interaction encoder to effectively learn more fine-grained and comprehensive second-order feature interactions. Taking advantages of the hierarchical attention mechanism and the projective bilinear function, our proposed model can not only model feature learning in a flexible fashion, but also provide an interpretable capability of the prediction results. Experimental results on two real-world datasets demonstrate that HAFP outperforms the state-of-the-art in terms of Logloss and AUC for CTR prediction baselines. Further analysis verifies the importance of the proposed hierarchical attention mechanism and the projective bilinear function for modelling the feature representation, showing the rationality and effectiveness of HAFP.