DCNv3: Towards Next Generation Deep Cross Network for CTR Prediction

• URL: http://arxiv.org/abs/2407.13349v6
• Date: Fri, 09 Aug 2024 06:31:56 GMT
• Title: DCNv3: Towards Next Generation Deep Cross Network for CTR Prediction
• Authors: Honghao Li, Yiwen Zhang, Yi Zhang, Hanwei Li, Lei Sang, Jieming Zhu,
• Abstract summary: 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. Comprehensive experiments on six datasets demonstrate the effectiveness, efficiency, and interpretability of DCNv3.
• Score: 17.19859591493946
• License:
• Abstract: 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.

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