Click Through Rate (CTR) prediction plays an essential role in recommender systems and online advertising. It is crucial to effectively model feature interactions to improve the prediction performance of CTR models. However, existing methods face three significant challenges. First, while most methods can automatically capture high-order feature interactions, their performance tends to diminish as the order of feature interactions increases. Second, existing methods lack the ability to provide convincing interpretations of the prediction results, especially for high-order feature interactions, which limits the trustworthiness of their predictions. Third, many methods suffer from the presence of redundant parameters, particularly in the embedding layer. This paper proposes a novel method called Gated Deep Cross Network (GDCN) and a Field-level Dimension Optimization (FDO) approach to address these challenges. As the core structure of GDCN, Gated Cross Network (GCN) captures explicit high-order feature interactions and dynamically filters important interactions with an information gate in each order. Additionally, we use the FDO approach to learn condensed dimensions for each field based on their importance. Comprehensive experiments on five datasets demonstrate the effectiveness, superiority and interpretability of GDCN. Moreover, we verify the effectiveness of FDO in learning various dimensions and reducing model parameters. The code is available on \url{https://github.com/anonctr/GDCN}.

翻译：点击通过率（CTR）预测在推荐系统和在线广告中扮演着重要角色。有效建模特征交互对于提升CTR模型的预测性能至关重要。然而，现有方法面临三大挑战：首先，虽然多数方法能自动捕获高阶特征交互，但其性能会随特征交互阶数增加而衰减；其次，现有方法难以对预测结果提供令人信服的解释，特别是针对高阶特征交互，这限制了其预测的可信度；第三，许多方法存在冗余参数问题，尤其是在嵌入层。本文提出一种名为门控深度交叉网络（GDCN）的新方法及场级维度优化（FDO）策略以应对这些挑战。作为GDCN的核心结构，门控交叉网络（GCN）能够捕获显式高阶特征交互，并通过每层的信息门动态筛选重要交互。此外，我们采用FDO方法基于各场的重要性为其学习精简维度。在五个数据集上的综合实验证明了GDCN的有效性、优越性和可解释性。同时，我们验证了FDO在学习差异化维度及减少模型参数方面的有效性。代码已开源至\url{https://github.com/anonctr/GDCN}。