DCNv3: Towards Next Generation Deep Cross Network for CTR Prediction

Deep & Cross Network and its derivative models have become an important paradigm in click-through rate (CTR) prediction due to their effective balance between computational cost and performance. However, these models face four major limitations: (1) while most models claim to capture high-order feature interactions, they often do so implicitly and non-interpretably through deep neural networks (DNN), which limits the trustworthiness of the model's predictions; (2) the performance of existing explicit feature interaction methods is often weaker than that of implicit DNN, undermining their necessity; (3) many models fail to adaptively filter noise while enhancing the order of feature interactions; (4) the fusion methods of most models cannot provide suitable supervision signals for their different interaction methods. To address the identified limitations, this paper proposes the next generation Deep Cross Network (DCNv3) and Shallow & Deep Cross Network (SDCNv3). These models ensure interpretability in feature interaction modeling while exponentially increasing the order of feature interactions to achieve genuine Deep Crossing rather than just Deep & Cross. Additionally, we employ a Self-Mask operation to filter noise and reduce the number of parameters in the cross network by half. In the fusion layer, we use a simple yet effective loss weight calculation method called Tri-BCE to provide appropriate supervision signals. Comprehensive experiments on six datasets demonstrate the effectiveness, efficiency, and interpretability of DCNv3 and SDCNv3. The code, running logs, and detailed hyperparameter configurations are available at: https://anonymous.4open.science/r/DCNv3-E352.