Deep & Cross Network and its derivative models have become an important paradigm for click-through rate (CTR) prediction due to their effective balance between computational cost and performance. However, these models face four major limitations: (1) the performance of existing explicit feature interaction methods is often weaker than that of implicit deep neural network (DNN), undermining their necessity; (2) many models fail to adaptively filter noise while increasing the order of feature interactions; (3) the fusion methods of most models cannot provide suitable supervision signals for their different sub-networks; (4) while most models claim to capture high-order feature interactions, they often do so implicitly and non-interpretably through DNN, which limits the trustworthiness of the model's predictions. To address the identified limitations, this paper proposes the next generation deep cross network: Deep Cross Network v3 (DCNv3), along with its two sub-networks: Linear Cross Network (LCN) and Exponential Cross Network (ECN) for CTR prediction. DCNv3 ensures interpretability in feature interaction modeling while linearly and 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 multi-loss trade-off and calculation method, called Tri-BCE, to provide appropriate supervision signals. Comprehensive experiments on six datasets demonstrate the effectiveness, efficiency, and interpretability of DCNv3. The code, running logs, and detailed hyperparameter configurations are available at: https://github.com/salmon1802/DCNv3.

翻译：深度交叉网络及其衍生模型因其在计算成本与性能之间的有效平衡，已成为点击率预测的重要范式。然而，这些模型面临四个主要局限：（1）现有显式特征交互方法的性能通常弱于隐式深度神经网络，削弱了其必要性；（2）许多模型在提升特征交互阶数时未能自适应地过滤噪声；（3）大多数模型的融合方法无法为其不同子网络提供合适的监督信号；（4）尽管多数模型声称能捕获高阶特征交互，但它们往往通过DNN以隐式且不可解释的方式实现，这限制了模型预测的可信度。为解决上述局限，本文提出了下一代深度交叉网络：Deep Cross Network v3（DCNv3），及其用于CTR预测的两个子网络：线性交叉网络与指数交叉网络。DCNv3在确保特征交互建模可解释性的同时，以线性和指数方式提升特征交互阶数，实现真正的深度交叉，而非仅仅是“深度与交叉”。此外，我们采用自掩码操作来过滤噪声，并将交叉网络的参数量减少一半。在融合层，我们使用一种简单而有效的多损失权衡与计算方法（称为Tri-BCE）来提供适当的监督信号。在六个数据集上的综合实验证明了DCNv3的有效性、高效性和可解释性。代码、运行日志及详细超参数配置可见于：https://github.com/salmon1802/DCNv3。