FDENet: Improving CTR Prediction via Feature Dynamic Enhancement

Click-through rate(CTR) prediction is a key task in the fields of e-commerce recommendation and online advertising, which needs to integrate various features of the item side and the user side, and has considerable complexity. Many works have made improvements in feature interaction learning, while a few have focused on improving feature representation. However, these works fail to capture both the feature context information and the contribution of features to CTR prediction task to improve the expression ability of the model. Therefore, to fill this gap, the CTR model for Feature Dynamic Enhancement(FDENet) is proposed. Specifically, for each sample, the model learns context aware feature representation by blending original features and complementary features, learns contribution aware feature representation by selecting salient features, and then combines the two to generate a unique adaptive dynamic feature representation of the sample, thereby achieving feature dynamic enhancement. Moreover, it integrates feature dynamic enhancement with high-order feature interactions into a unified architecture, achieving mutual enhancement and effectively improving the expression ability and prediction performance of the model. Extensive experiments on two real-world datasets show that the proposed model achieves better performance compared to the most relevant and advanced baselines.